AI in Preclinical and Clinical Development Fundamentals Training by Tonex
This 2-day intermediate-level training explores the transformative role of Artificial Intelligence in accelerating drug discovery, optimizing clinical trials, and enhancing operational efficiencies in preclinical and clinical development. Participants will learn how AI is reshaping key elements such as ADMET prediction, protocol optimization, and patient recruitment. The course also sheds light on the cybersecurity implications of AI integration in healthcare R&D, including the protection of sensitive trial data and ethical management of AI-generated insights. As AI penetrates deeper into life sciences, cybersecurity professionals play a crucial role in safeguarding data integrity and compliance in an evolving digital landscape.
Audience:
- Clinical operations professionals
- Regulatory affairs specialists
- Biostatisticians and data scientists
- AI engineers and algorithm developers
- Research and development managers
- Cybersecurity professionals involved in healthcare and clinical data protection
Learning Objectives:
- Understand how AI accelerates preclinical development workflows
- Explore AI-driven optimization of clinical trial design and execution
- Learn strategies for AI-based patient matching and recruitment
- Gain insight into predictive modeling of ADMET properties
- Analyze the use of synthetic control arms and virtual cohorts
- Address cybersecurity and ethical implications of AI use in trials
Course Modules:
Module 1: AI in Preclinical Research
- Role of AI in early-stage discovery
- Predictive modeling of ADMET properties
- High-throughput screening optimization
- AI-guided lead compound identification
- Automation in target validation
- Cybersecurity challenges in early data sharing
Module 2: Animal Model Selection
- AI algorithms for phenotype matching
- Genomic data integration strategies
- Cross-species prediction modeling
- Risk mitigation using AI analytics
- Translational value assessment with AI
- Data protection in experimental research
Module 3: Clinical Trial Design Optimization
- Protocol design using AI feedback loops
- Historical trial data analysis
- Trial feasibility prediction
- Adaptive design frameworks
- Inclusion/exclusion criteria modeling
- Regulatory security concerns and audit trails
Module 4: Patient Recruitment and Matching
- AI-assisted patient profiling
- EHR data mining for eligibility
- NLP for unstructured clinical notes
- Automated risk stratification
- Trial matching platforms and tools
- Securing personal health data
Module 5: Synthetic Control Arms
- Building virtual cohorts with AI
- Statistical models for comparison
- Use cases in rare diseases
- Ethical perspectives in synthetic data
- Regulatory pathways and validation
- Protecting model integrity and privacy
Module 6: AI for Trial Monitoring
- Real-time protocol deviation detection
- Predictive retention analytics
- NLP in adverse event reporting
- Monitoring site compliance via AI
- Integrating wearables and remote data
- Ensuring secure data transmission
Advance your expertise at the intersection of AI and clinical development with Tonex. Join this comprehensive training to harness AI’s potential in life sciences while addressing key cybersecurity and regulatory demands. Register now to secure your spot and lead the future of intelligent clinical research.