Predicting trial endpoints and outcomes using AI to improve efficiency and success rate, G.Pradeeptha reddy.pdf
- 1. Welcome Predicting trail endpoints and outcomes using AI to improve efficiency and success rates G. Pradeeptha Reddy M. Pharmacy 179/072024 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 1
- 2. Index ➢ Introduction ➢ Applications ➢ InClinico platform for predicting trial endpoints ➢ Potential of InClinico ➢ Advantages of AI ➢ Challenges of AI ➢ Conclusion ➢ References 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 2
- 3. Introduction What is AI? • Artificial Intelligence (AI) refers to the development of computer systems capable of performing tasks that typically require human intelligence. These tasks include reasoning, learning, problem- solving, perception, and language understanding • Artificial Intelligence Can Turn Eroom’s Law into Moore’s Law 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 3
- 4. Applications of AI in Clinical trails 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 4
- 5. InClinico in predicting success rate • InClinico is a platform for Data-driven multimodal forecast of single clinical trial's probability of success (PoS). • InClinico utilizes massive amounts of data about the targets, diseases, clinical trials, and even scientists involved with the study at the preclinical and clinical stages. 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 5
- 6. InClinico has undergone extensive validation to ensure its accuracy and reliability. The platform was evaluated through retrospective, quasi-prospective, and prospective validation studies, both internally and in collaboration with pharmaceutical companies and financial institutions. These validations demonstrated the platform’s ability to predict clinical trial outcomes effectively. 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 6
- 7. How accurate is inClinico in predicting phase II to phase III transition? 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 7 The transition from phase II to phase III clinical trials is a critical InClinico achieved ROC AUC of 0.88 in predicting the phase II to phase III transition on a quasi- prospective validation dataset The forecasted outcomes for several phase II clinical trials showed a remarkable 79% accuracy for the trials that had reached their readout stage
- 8. • The power of inClinico goes beyond its predictive capabilities. Remarkably, this approach demonstrated a 35% return on investment within a nine-month period. • Companies can allocate resources and prioritize clinical trials based on reliable predictions. This would reduce the economic burden of failed trials. • Validation and integration with other tools and techniques are necessary to fully leverage the potential of artificial intelligence in predicting clinical trial outcomes. • Pharmaceutical companies and regulatory agencies ought to embrace advancements in technology like inClinico to revolutionize the drug development landscape. 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 8 Real-World Examples and the Potential of inClinico
- 9. • Reducing costs by reducing the time required for study enrollment • Increasing participation • Decreasing intervention delivery burden and supervision, • Improving intervention fidelity when using smart apps and AI algorithms • Reducing data collection burden • Improving data quality, reproducibility, privacy improving analysis of big data when using AI and ML algorithms 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 9 Advantages of using AI in Clinical Trials
- 10. Challenges of using AI in Clinical Trials • Maintaining privacy/confidentiality of participant data • Ensuring that regulatory guidance reflects the latest technological advances • Ensuring access to adequate infrastructure, resources, and staff expertise • Keeping participants engaged and increasing retention • Considering user attitudes, practices, expectations, and preferences. • Ensuring data quality and accuracy • Overcoming the complexities surrounding big data 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 10
- 11. Conclusion • The ability to predict clinical trial outcomes has the potential to revolutionize the drug discovery and development process. Through the use of artificial intelligence, specifically the inClinico platform, researchers can harness the power of advanced algorithms and multi-modal data to accurately forecast the success of clinical trials. • With an impressive accuracy, inClinico provides a valuable tool for pharmaceutical companies and researchers alike. By prioritizing therapeutic programs that have a higher likelihood of success, resources can be optimized, and the overall efficiency and drug development decisions can be enhanced. 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 11
- 12. References 1. Alex Aliper, Roman Kudrin, Daniil Polykovskiy, Petrina Kamya, Elena Tutubalina, Shan Chen, Feng Ren, Alex Zhavoronkov; Prediction of Clinical Trials Outcomes Based on Target Choice and Clinical Trial Design with Multi-Modal Artificial Intelligence; 22 July 2023 2. Carmen Rosa, Lisa A Marsch, Erin L Winstanley, Meg Brunner, Aimee N C Campbell; Using digital technologies in clinical trials: current and future applications; 2022 Jan 6 3. Predicting Clinical Trial Outcomes: The Role of Artificial Intelligence and inClinico 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 12
- 13. Thank You! www.clinosol.com (India | Canada) 9121151622/623/624 info@clinosol.com 10/18/2022 www.clinosol.com | followus on social media @clinosolresearch 13