Innovative AI-enabled Clinical Trial Companies: Strategic Profiling and Growth Opportunities

The Impact of the Top 3 Strategic Imperatives on the AI-enabled Clinical Trials Industry

Disruptive Technologies

Why:

• The COVID-19 pandemic initiated a chain reaction with respect to increased R&D activities, with a resultant burden on pharmaceutical companies to provide innovative therapies.
• The growing prevalence of oncology, central nervous system (CNS) disorders, and cardiovascular diseases (CVDs) is propelling clinical research in these therapy areas, with more than 6,000 molecules in clinical development (including all therapy areas).

Frost Perspective:

• AI-based platforms are developing cutting-edge solutions, such as digital twins and synthetic and external control arms (ECAs), to improve trial efficiency and outcomes, especially in oncology trials requiring greater patient sample sizes.
• Leveraging AI-powered platforms in clinical trials can improve compliance with trial protocols and the accuracy of endpoint assessment in the next 3–5 years.

Industry Convergence

Why:

• With surging clinical trial pipelines, the associated challenges pertaining to trial recruitment, patient adherence, and execution are impacting therapeutic outcomes.
• Technology platforms in the form of AI play an important role in streamlining trial workflows to achieve the desired outcomes.

Frost Perspective:

• The pharmaceutical ecosystem, especially drug development, will continue to expand beyond the traditional sponsor/contract research organization (CRO) relationship, with the ingress of technology vendors in the next 2–3 years.
• AI vendors are creating a strong position with cutting-edge drug discovery, development, and manufacturing solutions, improving trial success rates.

Transformative Megatrends

Why:

• Small-to-mid segment and virtual biopharmaceutical companies are primarily driving pharmaceutical innovation (in the form of targeted therapies), resulting in increasing competition.
• Tech-enabled solutions are eliminating the need for lengthy and costly drug development processes through cutting-edge platforms.

Frost Perspective:

• Biopharmaceutical companies will continue to capitalize on AI-driven solutions to support their clinical research activities as the diversity in trials increases.
• The industry will witness more partnerships with AI vendors in the next 5–7 years, with a focus on improved compliance rates and reduced operational costs for clinical trials.

Scope of Analysis

• This study provides insight into the various AI platforms across the clinical trial value chain while offering strategic profiles of the industry’s unique and game-changing participants.
• It aims to analyze how AI can be leveraged to take a proactive approach to conducting seamless and tech-enabled efficient clinical trials. Despite its low uptake, AI presents a significant opportunity to cost-efficiently scale clinical research.
• The scope of this study covers AI use in pharmaceutical clinical trials only. The commercialization scenarios after phase IV and AI use in drug discovery are not included.
• The study takes into consideration various AI components, including machine learning (ML), deep learning (DL), and natural language processing (NLP), and their application in supporting clinical trial phases. It focuses on 4 application areas of AI in clinical trials—trial design, patient recruitment, enrichment, and enrollment, site selection, and patient monitoring. The study takes into consideration use cases to explore the underlying challenges and demand for AI in respective clinical trial applications and evaluates the growth opportunities in the industry with respect to the available AI-based solutions.
• The study focuses on AI algorithms that are commercially available and have received regulatory approval/clearance (e.g., US Food and Drug Administration (FDA) and CE marking).
• This qualitative research does not provide revenue forecast analysis for the industry segments.

Segmentation

AI in Clinical Trials

Clinical Trial Design

Clinical trial design is the formulation of trial protocols, experiments, and observational studies in clinical research involving human beings. AI collects and analyzes the growing data volumes that historical trials generate and extracts meaningful datasets to help with trial designs.

Patient Enrichment, Recruitment, and Enrollment

Patient recruitment involves finding, screening, and enrolling the right patient pool through a multifaceted approach. AI can identify suitable cohorts for clinical trials, simplifying the process for the right patient population.

Investigator and Site Selection

Site selection is a detailed evaluation of project needs measured against the merits of potential locations. Sponsors select the site by taking a feasibility survey tailored to their study. AI helps identify sites where a suitable patient pool for the study might be available.

Patient Monitoring, Adherence, and Retention

Analysis of medication adherence entails implementing and insisting on the recommended dosage. AI-driven platforms can analyze patient behavioral patterns and predict possible noncompliance to boost retention rates.

Growth Drivers

Growing Focus on Precision Health:

As the industry shifts its attention to a more patient-centric approach to drug development, trial designs are following suit, with AI platforms identifying the right patient cohorts for individual trials, making outreach easier for sponsors and ensuring higher success rates.

Achieving Economies of Scale with Process Automation:

Owing to growing trial volumes, AI-powered tools support various services, including RWD/RWE generation, and predictive analytics tools allow trial design and patient identification and recruitment platforms to automate the entire clinical trial value chain. In addition, AI-based platforms ease the recruitment process with user-friendly mobile interfaces and simpler recruitment processes that remotely support participation and address patient queries, building greater confidence.

Easy Data Integration Enabling Large-scale Data Availability:

AI uses integrated datasets from disparate sources as clear-cut insights in the form of user-friendly dashboards and graphical representations (knowledge graphs), making the information available for data pulls according to the sponsor’s requirements.

Growth Restraints

Challenges with Retrospective Studies Using Historical Data: While AI vendors continue to gather vast databases to train their in-house ML models, most of these platforms rely heavily on historical databases as opposed to RWD, greatly impacting the expected outcome of the actual clinical trial. This leads to the need for more prospective clinical trials that allow capturing real-time data using wearable devices and other gadgets.

Inability to Correctly Match Clinical Applicability to Individual Patients: Although AI/ML algorithms have been successfully predicting potential positive trial outcomes and supporting improved trial designs, these designs do not align well with individual patient care workflows by highlighting both positive and negative outcomes. This could potentially result in greater trial failures, despite successful outcomes in ML simulations.

Higher Initial Investments: With regulatory agencies such as the FDA considering AI-based platforms as medical devices, abiding by clinical, analytical, and technical validation regulations is imperative for safety. As a result, significant investments are necessary to implement these technologies, which may not be easy for the small-to-mid-sized segment and virtual biopharmaceutical companies with lower budgets that only support drug discovery and development.