Data-Driven Site Selection: Leveraging Machine Learning

Selecting optimal sites for clinical trials is crucial for the success of a study. Traditionally, site selection has relied on historical performance and investigator relationships, often resulting in time-consuming and subjective decisions. Leveraging machine learning (ML) for data-driven site selection offers a more systematic, efficient, and accurate approach, ensuring higher recruitment rates, better compliance, and overall trial success.

1. Welcome Data driven site selection : Leveraging Machine learning Mayank M. Pharm (Pharmacology) 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 1

2. 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 2 Introduction • ML is Crucial for trial success, influencing patient recruitment, data quality, regulatory compliance, and study timelines. • It ensures access to suitable patient population, qualified investigators, and necessary infrastructure. • The current challenges include geographic and demographic disparities, limited patient pools, varying site experience, and complex regulatory environments. • Traditional reliance on existing networks and historical data may overlook potential high-performing new sites. • ML helps in addressing challenges requires advanced data analytics, collaborative relationships, and adaptive strategies.

3. 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 3 Goals of Leveraging Machine Learning in Site Selection • Enhances decision-making, efficiency, and accuracy in identifying optimal trial locations. • ML algorithms analyze vast amounts of historical and real-time data, uncovering patterns and insights. • Enables informed decisions about site suitability based on patient demographics, investigator performance, and site infrastructure. • Automates data analysis, reducing time and resources required for site selection. • ML models continuously learn and adapt from new data, improving predictive accuracy. • Integrated ML enhances precision and reliability of decisions, optimizing trial outcomes and accelerating the path to new medical treatments.

4. Data Collection and Data Processing 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 4 • Patient data : Demographics, medical history, and genetic information for candidate identification and trial outcomes. • Healthcare provider data : Credentials, experience, and performance metrics of doctors and staff. • Geographic data : Population density and disease prevalence for optimal patient pools. • Clinical data : Medical records, treatment histories, and outcomes for trial evidence base. • Regulatory data : Compliance requirements, approval processes, and legal considerations for trials. • Social and economic data : Socioeconomic status of patient populations for trial participation and retention.

5. Exploratory Data Analysis (EDA) 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 5 • EDA : Preliminary examination of data sets to identify patterns, anomalies, and relationships. • Data visualization : Use of histograms, box plots, scatter plots, and heatmaps to understand complex data sets. • Statistical analysis : Use of mathematical methods to quantify relationships, test hypotheses, and derive conclusions. • Techniques : Regression analysis, t-tests, chi-square tests, and survival analysis. • Together, these components ensure rigorous, accurate, and interpretable results in clinical trials.

6. Integrating ML Models Into Trails Process 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 6 • Deployment strategies : Selecting appropriate platforms, ensuring model scalability, maintaining data security, and establishing continuous monitoring. • Visualization tools : Translating complex ML outputs into intuitive formats for better decision-making and communication. • APIs for real-time analysis : Enabling seamless data exchange and real-time processing, enabling dynamic adjustments during the trial. • These elements ensure ML models are embedded into clinical trial workflows, improving outcomes through enhanced data analysis and decision support.

7. ML Applications in Site Selection or Real World Application 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 7 • Pharmaceutical companies use ML to analyze vast datasets for site selection. • Leading biopharmaceutical company uses ML to streamline site selection for oncology trials. • ML models identify high-performing sites, reducing trial initiation time and cost. • Global Contract Research Organization uses ML to predict patient enrollment rates and site engagement levels. • ML-driven site selection leads to precise, data-informed decisions, enhancing trial efficiency and success rate.

8. 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 8 ML Workflow in Site Selection

9. Conclusion 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 9 • ML Enhances decision-making through data-driven insights. • Improves efficiency by streamlining selection process. • Increases accuracy in identifying optimal sites. • Leads to faster patient recruitment, better resource allocation, and higher trial success rates. • Future opportunities include integrating diverse data sources, developing adaptive algorithms, and expanding real-time analytics. • Revolutionizes clinical trial management for more effective and timely medical research outcomes.

10. Thank You! www.clinosol.com (India | Canada) 9121151622/623/624 [email protected] 14/06/2024 www.clinosol.com | follow us on social media @clinosolresearch 10