Clinical trials management

Explore the space beyond AI for drug discovery to realize quick gains through efficient management of clinical trials using AI

Clinical trials

Patient matching & recruitment

Identifying the right patients for a trial is essential for determining the safety and efficacy of potential drug molecules. Patient matching also affects site selection and start-up which is an important and time consuming step during a trial.

AI models can match the requirements on clinicaltrials.gov with patient health records to identify the right patients for the trials and improve patient recruitment and site selection.

Risk Based Monitoring (RBM)

Recent FDA guidance on Risk Based Monitoring (RBM) emphasizes focus on matters that are less likely to be a risk too. Hence, detecting anomalies is important e.g., data entry at strange times of the day.

AI models can flag such anomalies which can be investigated by Clinical Research Associates (CRA). Thus, CRA visits can be prioritized and made more effective.

Regulatory filing & compliance

Chemistry and Manufacturing Controls (CMC) regulatory and Safety update submissions may require information extraction from multiple documentation systems - Electronic Lab Notebooks (ELNs), Lab Information Mgmt Systems (LIMS) etc.

AI enables intelligent information retrieval and reconciliation from multiple systems through a combination of Robotic Process Automation (RPA) and Natural Language Generation (NLG). So, information can be automatically pre-filled into formats such as Investigator’s Brochure (IB) etc.