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Base complementarity between the three base pairs of mRNA codon and the tRNA anticodon is not a failsafe mechanism. Inaccuracies can range from a single mismatch to no correct base pairing at all. The free energy difference between the correct and nearly correct base pairs can be as small as 3 kcal/ mol. With complementarity being the only proofreading step, the estimated error frequency would be one wrong amino acid in every 100 amino acids incorporated. However, error frequencies observed in...
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Large Language Models for Clinical Trial Protocol Assessments.

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|October 22, 2025
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Summary
This summary is machine-generated.

Large language models (LLMs) show promise in reviewing clinical trial protocols, specifically the statistical analysis plan (SAP) and pharmacokinetics-pharmacodynamics (PK-PD) components. These AI tools can effectively extract and summarize technical details, aiding regulatory review processes.

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Area of Science:

  • Clinical trial methodology
  • Artificial intelligence in drug development
  • Regulatory science

Background:

  • Clinical trial protocols require rigorous review of statistical analysis plans (SAP) and pharmacokinetics-pharmacodynamics (PK-PD) components.
  • Ensuring compliance with regulatory guidelines, such as the FDA's E9 guidance, is critical for trial integrity.

Purpose of the Study:

  • To evaluate the effectiveness of large language models (LLMs) in reviewing the SAP and PK-PD sections of clinical trial protocols.
  • To assess the utility of LLMs as tools for regulatory experts in clinical trial review.

Main Methods:

  • Utilized GPT-4o (ChatGPT) to analyze 15 clinical trial protocols from clinicaltrials.gov.
  • Employed expert-persona prompts to elicit study design, guidelines, and SAP evaluations.
  • Assessed SAP methodology against FDA E9 guidance and evaluated PK-PD plans for accuracy and methods.

Main Results:

  • ChatGPT accurately identified disease, intervention, and comparator groups for all trials, and study sample size for 14/15.
  • LLM outputs were clear, organized, and demonstrated satisfactory extraction/summarization of technical details.
  • Some limitations in contextual accuracy were noted, but overall utility was demonstrated.

Conclusions:

  • LLMs, such as ChatGPT, can be effective tools for reviewing SAP and PK-PD components of clinical trial protocols.
  • These AI models show potential to assist in regulatory review by extracting and summarizing key technical information.
  • Further refinement may address observed limitations in contextual accuracy for comprehensive protocol assessment.