Path to Zero Failures in Pharma Manufacturing using AI and Automation

CASE INTRODUCTION

In the pharmaceutical manufacturing sector, batch failures and deviations in Critical Quality Attributes (CQAs) pose substantial risks, leading to significant financial, operational, and reputational damages. These disruptions can affect drug supply, elevate drug costs, and, most critically, endanger human lives. Hence, both pharmaceutical companies and regulatory agencies such as the FDA and EMA are dedicated to eradicating production failures to ensure public health and uphold the industry's integrity.

Root cause analysis for batch deviations typically spans 2 to 6 months, scrutinizing 1,000 to 10,000 parameters per batch. This extensive analysis involves Critical Process Parameters (CPP) and Critical Material Attributes (CMA) across numerous batches, relying on a non-predictive, manual, and partially electronic data extraction process. This method often fails to leverage past knowledge and depends heavily on individual expertise, leading to low confidence and inefficacy in preventing failures.

Nuron offers a transformative solution that automates deviation and root cause analysis, reducing the timeframe from months to mere hours or days. By employing an AI-driven, step-by-step, and data-centric approach, Nuron swiftly identifies the root causes of deviations and failures, moving away from intuition-based methods. Moreover, Nuron's machine learning capabilities provide real-time predictions of CQAs, failures, and deviations. This is achieved by integrating live production batch data with historical data from numerous previous batches. The automation of the ETSAD (Extraction, Transformation, Search, Analysis, and Decision-Making) workflow significantly enhances efficiency and accuracy, paving the path towards zero failures in pharmaceutical manufacturing.

PROBLEM

Batch failures and deviations in critical quality attributes (CQAs) cause significant financial, operational, and reputational losses for pharma companies.

These issues result in significant disruptions to drug supply, increased drug prices, and, most importantly, potential threats to human lives. Consequently, pharmaceutical companies and regulatory agencies, such as the FDA and EMA, are deeply committed to eliminating production failures to safeguard public health and maintain the integrity of the pharmaceutical industry.

• 5-10% batches have quality issues
• 62% Drug shortage caused by quality failures

CAUSE

Root cause analysis for batch deviations and failures takes 2 to 6 months, involving 1000-10,000 parameters per batch.

Critical process parameters (CPP) and material attributes (CMA) impacting quality attributes require analysis of data from 10s to 100s of batches.

The current manual and partially electronic data extraction process is non-predictive, postpartum, does not use past knowledge, and relies on individual experience, resulting in low confidence and an inability to prevent failures promptly.

Current Manual Process for CSR

• Paper Scans, Digital Data
• PDFs, Other Files
• Manual Data Extraction
• Manual Analysis
• Intuitive Root Cause Analysis

NURON DATA-TO-DECISION MAKING SOLUTION

Nuron automates the deviation and root cause analysis, completing the process in a matter of hours to days, a significant improvement over the traditional timeframe of months.

Nuron's solution utilizes an AI-driven, step-by-step, and data-driven approach rather than relying on individuals' intuition, to swiftly identify the root causes of deviations and failures.

Additionally, our machine learning capabilities enable real-time predictions of Critical Quality Attributes (CQAs), failures, and deviations.

This is achieved by seamlessly integrating live production batches' data and documents with information extracted from tens to hundreds of previous batches.

Automation of ETSAD Workflow

(Extraction, Transformation, Search, Analysis, and Decision-Making)

Step-by-step Data to Decision Workflow

GENAI POWERED REPORT ANALYSIS & WRITING

Nuron harnesses the power of GenAI to efficiently search and extract insights from various sources, including previous investigation reports, CAPA reports, equipment reports, and other relevant documents. Additionally, we deploy custom GenAI solutions to automate the process of report writing.