Part 2: How to Automate Drug Development with an AI-Native Approach?

Posted on: May 2nd 2025 

AI That Adapts, Learns, and Scales in Clinical Environments

Breaking the Rigidity of Clinical Protocols

Clinical trial procedures in the pharmaceutical industry have historically been strict and document-heavy. Even when new information indicates they should, these documents hardly change once set.

The assumptions underpinning trial protocols—dosage, endpoints, and eligibility requirements aren’t always verified in real time. This inflexibility limits the success of promising treatments by limiting researchers’ capacity to adapt to changing patient needs or trial conditions.

For instance, what if early data during a Phase II trial reveals that a subset of patients responds far better than others? Or does a slight dosage variation improve outcomes? Under conventional trial models, making those adjustments midstream is either impossible or risky, often due to regulatory restrictions, documentation overload, or poor data visibility.

With AI-Native solutions that enable trial protocols to adapt and compliance to be seamless, Straive tackles this issue at its root. Pharmaceutical companies can now manage trial designs as living-learning systems rather than static artifacts thanks to Straive’s real-time data ingestion, domain-trained algorithms, and expert-in-loop feedback.

This shift doesn’t just enhance accuracy—it helps trials stay on track, reduces dropout rates, and increases the odds of regulatory approval. And with built-in audit trails and GenAI-supported documentation, changes stay traceable, compliant, and review-ready.

Rethinking Compliance: From Bottleneck to Accelerator

Another major challenge in drug development is regulatory processes. Clinical documentation, health authority queries (HAQs), and labeling submissions are labor-intensive, frequently delayed, inconsistent, and error-prone. Rather than fostering innovation, compliance often acts as its most significant bottleneck.

Make way for Straive’s regulatory intelligence engine, purpose-built to bring speed, precision, and automation to compliance-heavy workflows. Here’s how Straive’s technology helps pharma companies:

• GenAI-powered HAQ Response Generation: Straive’s platform automatically creates high-quality, evidence-backed draft responses to regulator inquiries during the approval process by scanning prior submissions, trial data, and regulatory language.
• Dynamic labeling tools: Labels are more than just templates these days. By utilizing NLP and structured data processing to add or modify dosage information, adverse events, or efficacy notes, Straive makes sure they change with every trial update.
• Automated documentation workflows: Extracting and structuring data from adverse events, case studies, and trial reports into ready-for-submission content reduces human error and manual labor.

Real-World Example

A leading European pharma company collaborated with Straive to overhaul its regulatory documentation process. By embedding NLP and GenAI into the workflow and keeping domain experts in the loop, the company saved €5.2 million in operational costs (USD 5.9 million) while reducing the average time to submission by several weeks.

Straive’s approach digitizes and transforms old processes, resulting in quicker approvals, cleaner audits, and regulatory workflows that keep pace with innovation.

Fixing the Trial Bottleneck: Intelligent Patient Recruitment

When it comes to clinical trials, patient recruitment is arguably the most critical and also the most broken piece. Despite vast datasets and global outreach, most recruitment efforts rely on generalized outreach, static eligibility criteria, and fragmented physician networks. The outcome? Trials stop before they start. Or, even worse, they sign up the incorrect people, running the risk of biased findings and legal issues.

Straive changes the narrative here by blending advanced AI with life sciences expertise to ensure the right patients are recruited more quickly, precisely, and economically.

Here’s what Straive brings to the table:

• AI-driven patient stratification: Based on trial parameters, our AI tools predict the best patient matches by analyzing both structured and unstructured datasets, including trial databases and electronic health records.
• HCP and patient data enrichment: We unify fragmented datasets, clean noisy inputs, and layer in demographic, geographic, and clinical data to sharpen outreach.
• Personalized engagement at scale: Physicians and patients receive customized trial information based on real-world fit rather than generic inclusion/exclusion checklists through outreach engines and automated summarization tools.

This reflects the approach of several leading global pharma firms, which have partnered with AI-led patient data platforms to improve stratification in oncology trials. Straive provides similar precision but with additional scalability and real-time adaptability infrastructure.

Straive helps companies reduce recruitment timelines, improve cohort quality, and improve trial data by reducing false fits and delays from the beginning.

Straive’s AI-Native Stack Under The Hood

An integrated platform intended for innovation and large-scale, production-grade deployment powers everything covered thus far. Straive’s AI-Native stack goes beyond modeling; it’s a full-fledged ecosystem purpose-built for pharma.

LLM Foundry

LLM Foundry is our proprietary platform designed to facilitate the adoption and integration of Large Language Models (LLMs) into various applications, including healthcare and pharma. It provides tools and resources for developers to easily access and manage different LLMs while ensuring that the implementation meets security and cost-efficiency requirements. With features such as proxy access to multiple LLM providers, usage auditing, and support for low-cost deployment, LLM Foundry empowers healthcare organizations to leverage the capabilities of AI-driven language processing while maintaining control over their usage and expenses.

Biomedical Data Pipelines

Massive amounts of clinical data, scientific literature, and regulatory updates are normalized and contextualized by Straive’s pipelines, which quickly feed reliable, usable data into your systems.

Expert-in-Loop Copilots

Whether generating HAQ responses or labeling updates, our copilots combine AI speed with human judgment to ensure every insight or draft is backed by SME validation.

AI Accelerators

Purpose-built tools to automate repetitive, document-heavy tasks—like protocol generation, submission compilation, or safety report creation. These accelerators are ready to plug and play with pharma systems and have delivered up to 30% time-to-market reductions.

Straive does more than enable AI with this infrastructure. We make it safe, scalable, and sustainable in the world’s most compliance-heavy industry.

Last Part: The Proof Is in the Results

In the penultimate part of this three-part blog, we will showcase real-world case studies—from biomedical annotation at scale to end-to-end clinical trial automation, and show how Straive’s AI-Native approach delivers measurable, high-impact results.

You will see why global pharma companies aren’t just adopting AI but are operationalizing it with Straive.

Stay tuned for Part 3: Proven Outcomes and Why Straive Leads the Way.

About the Author

Santosh Shevade

Santosh Shevade is a Principal Data Consultant at Gramener – A Straive Company. With deep expertise in healthcare strategy, digital health, and clinical development and operations, he has supported nearly 50 clinical development programs across all clinical phases. His experience spans advanced analytics solution design for pharmaceutical companies, mHealth implementation, and AI applications in healthcare. Previously at Novartis and Johnson & Johnson, Santosh led global clinical development teams and streamlined data review processes for major regulatory submissions. A certified MBTI trainer and leadership coach, he serves as visiting faculty at ISB Hyderabad and Welingkar Institute, focusing on healthcare technology innovation and biopharma strategy.