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<p>The pharmaceutical industry is increasingly transitioning from reactive regulatory compliance toward predictive, intelligence-driven quality governance. This study develops and empirically validates a data-driven framework for predictive pharmaceutical compliance using FDA Form 483 inspection observations spanning FY2018-FY2024. By integrating advanced text analytics, a validated deficiency taxonomy, and supervised machine-learning models, the research transforms multi-year inspection data into actionable indicators of systemic risk, organizational maturity, and regulatory foresight. The analysis shows that data integrity and CAPA effectiveness are the dominant structural drivers of repeat regulatory exposure. At the same time, the severity composition of inspection findings functions as a quantitative proxy for quality system maturity. Predictive models demonstrate strong and stable discrimination, confirming that compliance recurrence risk can be quantified using routinely available inspection data. Sectoral analysis further reveals differentiated risk patterns across sterile, API, biotech, and finished-dosage manufacturing, despite shared underlying drivers related to digital governance, investigation depth, and human reliability. A central insight is the interdependence of digital precision and human capability. Facilities that combine validated electronic systems with disciplined training and governance practices experience materially lower recurrence risk, underscoring that predictive compliance is a sociotechnical transformation rather than a technology upgrade alone. The findings align with emerging global regulatory priorities, including the FDA Quality Management Maturity initiative, EMA quality innovation efforts, and MHRA data-integrity guidance. Together, these insights are synthesised into a Globally Aligned Predictive Compliance Model, reframing compliance as a strategic capability that enhances resilience, operational reliability, and sustained regulatory trust.</p>

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The Predictive Quality Shift: Transforming FDA 483 Data into a System for Digital-Behavioral Compliance Intelligence

  • 期刊论文

  • 发表日期：

    2026年3月20日

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  • Nagesh Patil,
  • Sonali Patil,
  • Mahesh Mane

摘要

The pharmaceutical industry is increasingly transitioning from reactive regulatory compliance toward predictive, intelligence-driven quality governance. This study develops and empirically validates a data-driven framework for predictive pharmaceutical compliance using FDA Form 483 inspection observations spanning FY2018-FY2024. By integrating advanced text analytics, a validated deficiency taxonomy, and supervised machine-learning models, the research transforms multi-year inspection data into actionable indicators of systemic risk, organizational maturity, and regulatory foresight. The analysis shows that data integrity and CAPA effectiveness are the dominant structural drivers of repeat regulatory exposure. At the same time, the severity composition of inspection findings functions as a quantitative proxy for quality system maturity. Predictive models demonstrate strong and stable discrimination, confirming that compliance recurrence risk can be quantified using routinely available inspection data. Sectoral analysis further reveals differentiated risk patterns across sterile, API, biotech, and finished-dosage manufacturing, despite shared underlying drivers related to digital governance, investigation depth, and human reliability. A central insight is the interdependence of digital precision and human capability. Facilities that combine validated electronic systems with disciplined training and governance practices experience materially lower recurrence risk, underscoring that predictive compliance is a sociotechnical transformation rather than a technology upgrade alone. The findings align with emerging global regulatory priorities, including the FDA Quality Management Maturity initiative, EMA quality innovation efforts, and MHRA data-integrity guidance. Together, these insights are synthesised into a Globally Aligned Predictive Compliance Model, reframing compliance as a strategic capability that enhances resilience, operational reliability, and sustained regulatory trust.