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Exploring Challenges and Opportunities for Improving Phase 2 and Phase 3 Clinical Trials: A Review

Bureddi Anudeep Venkata Sai1, Sangita Mishra1, M P Venkatesh1,2

  • 1Centre of Excellence in Regulatory Sciences, Dept. of Pharmaceutics, JSS College of Pharmacy, JSS Academy of Higher Education and Research, SS Nagara, Mysuru, 570015, Karnataka, India.

Reviews on Recent Clinical Trials
|April 5, 2026
PubMed
Abstract

Insights

Innovative clinical trial designs, including decentralized trials and AI/ML, enhance drug development efficiency and success rates. Addressing challenges like standardization and the digital divide is crucial for realizing their full potential.

Area of Science:

  • Drug Development
  • Clinical Trial Design
  • Biotechnology

Background:

  • Clinical trials face significant challenges including high costs, participant attrition, and operational inefficiencies.
  • Phase 2 and 3 studies are particularly impacted by these hurdles, slowing drug discovery.
  • Current issues in cost, safety, efficacy, and operations necessitate novel approaches.

Purpose of the Study:

  • To examine current issues in clinical trial design, focusing on cost, safety, efficacy, and operational inefficiencies.
  • To analyze the importance of novel approaches like decentralized trials, adaptive designs, digital endpoints, and AI/ML.
  • To identify strategies for improving trial efficiency, participant recruitment, data quality, and success rates in drug development.

Main Methods:

  • A mixed-method strategy was employed to study various clinical trial designs.
  • Analysis included adaptive trial designs, decentralized trials, and regulatory trial programs.
  • The study assessed the use of AI/ML models for improving enrollment, design strategies, and outcome prediction.

Main Results:

  • Decentralized trials (DCTs), adaptive trials, and digital endpoints have improved enrollment, speed, and precision.
  • AI/ML models optimize recruitment, trial designs, and outcome predictions, leading to more effective studies.
  • Despite progress, data integrity issues persist with the adoption of new trial methodologies.

Conclusions:

  • Innovative trial approaches enhance efficiency, reduce costs, and increase the likelihood of successful drug development.
  • Overcoming challenges in standardization, regulatory alignment, ethics, and the digital divide is key to realizing the full potential of these advancements.
  • A multi-faceted strategy integrating technological and methodological advancements is needed for a more agile, patient-centric clinical trial ecosystem.
Keywords:
Clinical trialsadaptive trialsartificial intelligencedrug developmentefficacymachine learning.safety

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