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1. Fundamentals of Artificial Intelligence (AI) in the company
- General introduction to AI: history and evolution of AI.
- Main AI concepts and terminology.
- Differences between Machine Learning, Deep Learning and other branches of AI.
- AI models and their general applications.
- AI work environment: core tools, languages, and platforms.
- Implementation of AI solutions in the company (Cloud vs on-premises).
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2. Introduction to AI in the pharmaceutical industry
- Introduction to the field of AI and its application in the pharmaceutical industry.
- Types of AI, key differences for their application in pharma.
- Regulation, ethics and data security.
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3. Drug research and discovery
- Using AI in the identification of new therapeutic targets.
- Use of algorithms for the generation of new compounds.
- Predictive modeling and simulations.
- Drug candidate optimization.
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4. Preclinical development
- Predictive models of toxicity and safety with AI.
- Optimization of pharmacokinetics and pharmacodynamics.
- In silico modelling.
- Use cases.
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5. Clinical trials
- Design and planning of clinical trials with AI.
- Optimizing patient recruitment with AI.
- Virtual clinical trials and the use of AI in remote monitoring.
- Clinical success and outcome prediction models.
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6. Regulatory affairs & legal department
- AI in the preparation and submission of regulatory documents.
- Monitoring of regulations and guidance from regulatory agencies.
- Contract negotiation.
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7. Production and distribution
- Predictive production maintenance.
- Implementing AI-based quality control systems.
- Inventory optimization. Logistics and distribution.
- Cold chain control.
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8. Marketing and sales
- Predictive analysis of market trends.
- Market segmentation.
- Collaborative strategies.
- Marketing and sales campaign optimization.
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9. Finance & controller
- Predictive analytics and financial and budget planning.
- Automation of financial processes and accounting.
- Fraud detection. Risk management and financial analysis.
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10. Pharmacovigilance and medical information
- Adverse event detection using NLP and machine learning.
- Automation of pharmacovigilance processes. Safety sign detection.
- Centralizing data in medical information.
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11. Human Rresources
- Recruitment and selection of personnel. Onboarding and training.
- Adaptative learning platforms.
- Talent management and prediction of development tools.
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12. Future of AI in the pharmeceutical industry
- Emerging trends, strategic planning and implementation.
- AI implementation roadmap by regulatory agencies.
- AI implementation costs, grants and subsidies.
- Future challenges and opportunities. Course discussion and evaluation.
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13. Final practices
- Preparing for the final exam.
- Review of key concepts learned in the course.
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