Pharmaceutical Microbiology & Quality Control: Environmental Monitoring and Contamination Investigation

Aim

Workshop Objectives

Workshop Structure

Day 0: Pre-Workshop Computational Setup & Microbial Dataset Preparation

Duration: 30 Minutes

• Introduction to workshop objectives, practical deliverables and the three-day workflow.
• Mentor-guided access to prepared microbial count, equipment-monitoring, sterilization-cycle and environmental monitoring datasets.
• Introduction to dataset fields, sample identifiers, measurement units and file organization.
• Google Colab setup, notebook access and saving personal copies.
• Loading and verifying pandas, NumPy, Matplotlib and Seaborn.
• Importing a sample CSV file and running the starter notebook.
• Technical readiness checks, troubleshooting and participant queries.

🛠️ Hands-On: Mentor-Assisted Dataset Access & Computational Setup

Open the prepared notebook, import a sample laboratory dataset, inspect its structure and verify the required libraries.

Practical Outcome: A working Colab environment and access to the workshop datasets and templates.

📅 Day 1: Microbial Counts, Data Quality & Laboratory Equipment Monitoring

• Principles of microbial quality control and reliable laboratory data.
• Cleaning datasets and identifying missing values, duplicate entries and inconsistent units.
• Calculating CFU/mL and CFU/g using dilution factors and plated volumes.
• Interpreting controls, replicates, countable ranges, zero counts and detection limits.
• Calculating mean, standard deviation and variability.
• Reviewing incubation conditions and equipment temperature records.
• Creating charts and documenting potential data-quality or equipment deviations.

🛠️ Hands-On: Analyse Microbial Counts & Equipment Records

Clean a prepared dataset, calculate microbial counts and replicate statistics, and examine equipment temperature trends.

Tools: Google Colab | pandas | NumPy | Matplotlib

Practical Outcome: Cleaned dataset, microbial-count summary, equipment charts and deviation observations.

📅 Day 2: Steam Sterilization Analysis & Cycle Deviation Assessment

• Principles of moist-heat sterilization and microbial inactivation.
• Understanding steam conditions, air removal, exposure time and load configuration.
• Distinguishing heat distribution from heat penetration.
• Interpreting chamber and load temperature profiles.
• Understanding D-values, z-values and F₀.
• Calculating cumulative F₀ using defined reference conditions.
• Comparing reference and deviating cycles.
• Reviewing physical records, chemical indicators and biological indicators together.

🛠️ Hands-On: Evaluate Sterilization Cycle Performance

Analyse prepared autoclave records, calculate F₀, compare cycles and identify missing evidence or follow-up checks.

Tools: Google Colab | pandas | NumPy | Matplotlib

Practical Outcome: Temperature and F₀ plots, cycle comparison and a structured deviation-assessment report.

📅 Day 3: Environmental Monitoring & Integrated Contamination Investigation

• Organising air, surface and personnel monitoring results.
• Comparing results under consistent sampling methods and reporting units.
• Visualising contamination trends by date and sampling location.
• Interpreting supplied site-specific alert and action levels.
• Identifying recurring excursions and contamination patterns.
• Integrating microbial, equipment, sterilization and environmental evidence.
• Separating observations, possible associations and root-cause hypotheses.
• Preparing proposed CAPA, follow-up investigations and effectiveness checks.

🛠️ Hands-On: Investigate a Contamination Case & Propose CAPA

Use a connected case study to create trend charts and heatmaps, assemble an evidence matrix and prepare an investigation report.

Tools: Google Colab | pandas | Matplotlib | Seaborn | Investigation and CAPA Templates

Practical Outcome: Monitoring visualisations, an evidence matrix and an investigation report with proposed CAPA.

Who Should Enrol?

Important Dates

Workshop Outcomes

Fee Structure