Automation and AI improve efficiency and accuracy while reducing the cost of new drug discovery

Challenge

New drug discovery is an expensive, slow, and risky process. Acquiring regulatory approval before new drugs can be placed on the market is highly challenging. The complete process can take ten years or more, with the average estimated cost of developing a new drug at around US$ 2.6 billion. Reducing these time frames and costs is a key goal of pharma research companies. Meeting the goal successfully directly impacts global health: Diseases can be treated faster, saving more lives; the cost of treatment can be lowered, making health care available to larger populations.

The client’s global medical writing team needed to automate the process of extracting information from the clinical study protocol and transferring it to PharmaCM, a clinical trial disclosure application central to managing regulatory compliance. This process was being managed manually by an external partner. The operations were slow, expensive, and error-prone, offering significant potential for improvement. To do this, the client needed to automate the process, resulting in improved process speed, boosting efficiency, and lowering costs.

Solution

The Marlabs team used RPA in the form of CODIbot (Content for Disclosure bot). CODIbot extracts the data and transfers it to PharmaCM. More than 80% of the data fields were automated. Once the data is entered into PharmaCM, CODIbot sends a summary report to the research team.

To do this successfully, the Marlabs automation experts collaborated with the client’s medical writing team to update the clinical study protocol template. This enabled further automation so the data could be reused. In addition, the confidentiality of protocol documents, an essential compliance requirement, was assured using appropriate storage and encryption processes.

The automation team classified PharmaCM data fields into three groups:

• Not applicable (18): Field not filled out in PharmaCM
• Not in scope (31): Data fields that require SME expertise to be completed, for example, information that cannot be extracted unambiguously from the clinical study protocol
• In scope (61): Data fields that the bot can fill as the information is available in the protocol

Now, when the client’s clinical trial protocol team creates a new study and PharmaCM uploads the data to a file share, an email is sent to start CODIbot. First, CODIbot checks to see if the file has been processed before. If not, it extracts pre-specified protocol content such as study title, secondary ID, brief summary, ARMS and intervention section, study endpoints, eligibility criteria, and some administrative details. To do this, CODIbot uses a set of validation rules. Then, the extracted data is entered into PharmaCM, and an email is sent with a summary of the outcome and the execution report. If the process is unsuccessful, the report shows the reason for the failure. In addition, errors are collected by the team to improve the process further.

The technologies used to create the automation included process mining, RPA, OCR, AI-based intelligent automation, and conversational AI.

Results

The implementation of automation and AI significantly accelerated the drug discovery process for the life sciences company. By streamlining data processing and experiment management, the organization reduced time-to-insight, enabling faster identification of potential drug candidates and more efficient use of research resources.

These enhancements also improved data accuracy and consistency, reducing manual errors and increasing confidence in research outcomes. The cost savings from automation allowed the company to reallocate resources to higher-value activities, driving innovation while maintaining regulatory compliance and improving overall productivity.

Impact

• Improved data quality
• Lowered cost of external vendors by shortening the review process and increasing the quality of registration postings
• Improved security in handling protocol documents that have confidential clinical trial information through the elimination of human intervention
• 99% reduction in manual effort
• 99% reduction in data entry errors
• 24/7 availability of the bot
• 175% increase in processing speed