Life sciences firm automates clinical trial design with AI

Life sciences firm automates clinical trial design with AI

Designing country strategies for clinical trials is one of the most costly and time-intensive stages of drug development. A global life sciences company wanted to leverage the latest advances in AI to change that. Modus Create helped the firm build an intelligent application that combines predictive enrollment models with constraint optimization to generate country strategies instantly. What once required days of manual analysis now takes minutes, giving analysts a quicker, more reliable way to plan trials and bring new treatments to market faster.

OUR WORK INVOLVED

• Research and service blueprints
• Rapid prototyping
• AI forecasting with trial data
• Constraint optimization engine
• Full-stack web app development

Every day a drug launch is delayed, pharmaceutical companieslose nearly $500,000.

The pressure to move fast is immense. However, rushing clinical trials is no solution either, as that can lead to regulatory setbacks, safety concerns, and costly fines.

The real challenge is accelerating timelines without compromising compliance or patient safety.One of the biggest friction points in this regard is designing country-specific trial strategies. Each market has its own regulatory framework, enrollment dynamics, and operational hurdles. Navigating these fragmented ecosystems requires a massive investment in both time and expertise.

Challenge

High costs and complexity in clinical trial country strategies

A global life sciences leader that develops clinical trial strategies for many of the world’s largest pharmaceutical companies faced the same issue. Its process for designing country-specific strategies was slow and expensive. Analysts had to manually weigh countless variables, such as patient enrollment rates, regulatory requirements, site capacity, and political risk.

This effort relied on siloed spreadsheets and institutional knowledge that lived largely “in analysts’ heads.” Every new constraint meant starting over, slowing down strategy creation by weeks. Analysts had a natural bias toward exhaustive analysis, but that instinct clashed with the business imperative to accelerate trial startups.

The company wanted to fix this manual, error-prone process using the latest advancements in AI and machine learning. It reached out to Modus Create to work on the problem.

Solution

Intelligent application for automating country trial strategies

Our full-stack, cross-discipline product development team joined the life sciences company to design a system for managing country clinical trial planning at scale. The platform combined three pillars:

• AI models trained on historical enrollment data
• Constraint-optimization engine to balance speed and cost
• An intelligent application for automating clinical trial country strategies that gave analysts direct control

Together, these elements turned country strategy design from a fragmented manual exercise into a structured, data-driven process.

1. Research and discovery

Before writing a single line of code, our team interviewed feasibility analysts, therapeutic leads, and medics, the people who lived the country clinical trial strategy process every day. They walked us through how strategies were built and discussed pain points around gathering enrollment data, weighing regulatory requirements, and debating site capacity.

To untangle this complexity, we created a service blueprint that captured roles, the sequence of decisions, the terminology analysts used, and the points where delays crept in. We also framed what success would look like with clear goals, such as:

• Reducing time to create country strategies by weeks
• Providing recommendations that analysts can trust
• Having enough flexibility in the solution to handle edge cases
• Demonstrating a clear impact on business ROI

This blueprint and the opportunities for innovation it yielded became the foundation for a high-value full-scale web application.

2. AI forecasting and constraint optimization

With the blueprint in place, we turned to intelligence. The pharma company’s data scientists brought historical trial data to the table with years of enrollment patterns across geographies. This helped train machine learning models capable of predicting how quickly patients could be enrolled in different countries.

Next, we paired the ML model with a constraint-based optimization engine. For the first time, analysts could ask the system real-world questions and get answers instantly, such as, What’s the fastest trial if we must include Japan? How do costs change if we cap patients per site at ten? Whathappens if we exclude a country due to political risk?

This turned strategy design from a static, linear process into an iterative scenario-based one, where analysts could explore, challenge, and validate, with AI as a partner rather than a black box.

3. Building an intelligent web application

Once the models and optimization engine were in place, we focused on delivery. Our team designed and built a cloud-hosted intelligent application on Heroku (running on AWS) that served as the front end for the system.The build process prioritized scalability, security, and a clean user experience, ensuring the application could integrate seamlessly into analysts’ workflows.

The new web application allowed the life sciences firm’s analysts to:

• Enter clinical trial parameters and constraints
• Generate optimized country strategies in minutes
• Visualize enrollment curves, projected costs, and site distributions by country
• Run “what-if” scenarios without starting over
• Override AI outputs with their own expertise

The application was shaped by clear design imperatives from discovery: don’t oversimplify, preserve statistical integrity, and make recommendations transparent. Analysts could see not only the outputs but the reasoning behind them.

Impact

Multi-million dollar savings and faster time to market

The intelligent application reinvented how country trial strategies are created. What once demanded weeks of manual analysis across spreadsheets is now handled in minutes through an intelligent, repeatable system. Analysts are no longer slowed by fragmented data and redundant recalculations. Instead, they can configure parameters, generate optimized scenarios instantly, and focus their expertise on making strategic decisions.

The app’s impact has been felt across four dimensions:

• Faster clinical trial strategies: Strategy design cycles that previously stretched over weeks are now compressed into minutes, freeing experts to focus on higher-value analysis rather than mechanical data work.
• Empowered analysts: Instead of being constrained by static spreadsheets, analysts can run alternate “what-if” scenarios, compare results side by side, and override AI outputs with their judgment. This combination of automation and control has been critical to adoption.
• Significant cost savings: Faster country trial strategies directly translate to faster clinical trials, leading to substantial cost savings.
• Improved information access: By embedding institutional expertise into a repeatable system, the platform ensures consistency in strategy creation and reduces dependence on a handful of senior experts.

Together, these benefits established a new standard for clinical trial planning. The life sciences company can now accelerate drug development timelines, contain costs, and scale strategy creation across global geographies, while maintaining compliance and rigor.