AI in Pharmaceutical Product Development

Categories: Health

About Course

Artificial Intelligence (AI) is transforming pharmaceutical R&D, formulation design, process development, and quality assurance. This module introduces participants to modern AI-driven tools, data science applications, and digital innovation strategies used in global pharmaceutical product development. The content aligns with ICH Q8–Q12, WHO TRS guidelines, and the digital maturity expectations under PIC/S Pharma 4.0 initiatives.

• To enable participants to understand the role of AI, machine learning (ML), and data analytics in drug development, formulation, and process design.
• To provide hands-on familiarity with digital tools that support predictive modeling, QbD design, and process optimization.
• To prepare participants to work in AI-assisted R&D environments, improving speed, quality, accuracy, and regulatory compliance.
• To enhance decision-making using real-time data analysis and AI-driven risk assessments.

Course Content

1. Introduction to AI in Pharma
• Concepts of AI, ML, and deep learning • Role of AI in Pharma 4.0 • Regulatory expectations for AI-based systems (WHO, PIC/S, FDA, EMA)

2. AI in Drug Discovery & Preformulation
• Predictive molecule–target interactions • Structure–activity relationship (SAR) modeling • AI-driven screening of excipients and active ingredients • Toxicity and stability prediction

3. AI-Assisted Formulation Development
• Machine learning for formulation optimization • Predictive modeling for solid dosage forms, sterile products, biologics • AI-driven dissolution and bioavailability forecasting • Digital design of experiments (DoE) tools

4. AI in Process Development
• AI-based scale-up and process simulation • Real-time process monitoring (PAT + AI) • Digital twins in pharmaceutical manufacturing • Reducing batch failures with predictive analytics

5. AI in Quality by Design (QbD)
• AI-enhanced identification of CQAs and CPPs • Risk management using ML algorithms • Automated root cause analysis (RCA) • Integration of AI into QMS

6. AI for Stability & Shelf-Life Prediction
• Predictive stability modeling • Real-time degradation analysis • Optimized storage condition simulations

7. AI in Clinical Research & Regulatory Submissions
• AI-based clinical trial optimization • Predictive patient response modeling • Use of AI in eCTD, dossier preparation, and regulatory intelligence

8. Ethical, Regulatory & Data Integrity Considerations
• WHO and PIC/S guidance on AI quality systems • Ensuring ALCOA+ compliance with automated data workflows • Bias prevention and model validation

Course Details

AI in Pharmaceutical Product Development

About Course

What Will You Learn?

Course Content

1. Introduction to AI in Pharma
• Concepts of AI, ML, and deep learning • Role of AI in Pharma 4.0 • Regulatory expectations for AI-based systems (WHO, PIC/S, FDA, EMA)

2. AI in Drug Discovery & Preformulation
• Predictive molecule–target interactions • Structure–activity relationship (SAR) modeling • AI-driven screening of excipients and active ingredients • Toxicity and stability prediction

3. AI-Assisted Formulation Development
• Machine learning for formulation optimization • Predictive modeling for solid dosage forms, sterile products, biologics • AI-driven dissolution and bioavailability forecasting • Digital design of experiments (DoE) tools

4. AI in Process Development
• AI-based scale-up and process simulation • Real-time process monitoring (PAT + AI) • Digital twins in pharmaceutical manufacturing • Reducing batch failures with predictive analytics

5. AI in Quality by Design (QbD)
• AI-enhanced identification of CQAs and CPPs • Risk management using ML algorithms • Automated root cause analysis (RCA) • Integration of AI into QMS

6. AI for Stability & Shelf-Life Prediction
• Predictive stability modeling • Real-time degradation analysis • Optimized storage condition simulations

7. AI in Clinical Research & Regulatory Submissions
• AI-based clinical trial optimization • Predictive patient response modeling • Use of AI in eCTD, dossier preparation, and regulatory intelligence

8. Ethical, Regulatory & Data Integrity Considerations
• WHO and PIC/S guidance on AI quality systems • Ensuring ALCOA+ compliance with automated data workflows • Bias prevention and model validation

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Course Details

About Course

What Will You Learn?

Course Content

1. Introduction to AI in Pharma • Concepts of AI, ML, and deep learning • Role of AI in Pharma 4.0 • Regulatory expectations for AI-based systems (WHO, PIC/S, FDA, EMA)

2. AI in Drug Discovery & Preformulation • Predictive molecule–target interactions • Structure–activity relationship (SAR) modeling • AI-driven screening of excipients and active ingredients • Toxicity and stability prediction

3. AI-Assisted Formulation Development • Machine learning for formulation optimization • Predictive modeling for solid dosage forms, sterile products, biologics • AI-driven dissolution and bioavailability forecasting • Digital design of experiments (DoE) tools

4. AI in Process Development • AI-based scale-up and process simulation • Real-time process monitoring (PAT + AI) • Digital twins in pharmaceutical manufacturing • Reducing batch failures with predictive analytics

5. AI in Quality by Design (QbD) • AI-enhanced identification of CQAs and CPPs • Risk management using ML algorithms • Automated root cause analysis (RCA) • Integration of AI into QMS

6. AI for Stability & Shelf-Life Prediction • Predictive stability modeling • Real-time degradation analysis • Optimized storage condition simulations

7. AI in Clinical Research & Regulatory Submissions • AI-based clinical trial optimization • Predictive patient response modeling • Use of AI in eCTD, dossier preparation, and regulatory intelligence

8. Ethical, Regulatory & Data Integrity Considerations • WHO and PIC/S guidance on AI quality systems • Ensuring ALCOA+ compliance with automated data workflows • Bias prevention and model validation

Follow Us

1. Introduction to AI in Pharma
• Concepts of AI, ML, and deep learning • Role of AI in Pharma 4.0 • Regulatory expectations for AI-based systems (WHO, PIC/S, FDA, EMA)

2. AI in Drug Discovery & Preformulation
• Predictive molecule–target interactions • Structure–activity relationship (SAR) modeling • AI-driven screening of excipients and active ingredients • Toxicity and stability prediction

3. AI-Assisted Formulation Development
• Machine learning for formulation optimization • Predictive modeling for solid dosage forms, sterile products, biologics • AI-driven dissolution and bioavailability forecasting • Digital design of experiments (DoE) tools

4. AI in Process Development
• AI-based scale-up and process simulation • Real-time process monitoring (PAT + AI) • Digital twins in pharmaceutical manufacturing • Reducing batch failures with predictive analytics

5. AI in Quality by Design (QbD)
• AI-enhanced identification of CQAs and CPPs • Risk management using ML algorithms • Automated root cause analysis (RCA) • Integration of AI into QMS

6. AI for Stability & Shelf-Life Prediction
• Predictive stability modeling • Real-time degradation analysis • Optimized storage condition simulations

7. AI in Clinical Research & Regulatory Submissions
• AI-based clinical trial optimization • Predictive patient response modeling • Use of AI in eCTD, dossier preparation, and regulatory intelligence

8. Ethical, Regulatory & Data Integrity Considerations
• WHO and PIC/S guidance on AI quality systems • Ensuring ALCOA+ compliance with automated data workflows • Bias prevention and model validation