The client is a transformative pharmaceutical company with a mission to enhance patient care by delivering affordable, high-value medications addressing unmet clinical needs. They specialize in identifying and commercializing safe, effective pharmaceutical products by leveraging business intelligence and data-driven product discovery methods. With a unique market approach focused on reducing regulatory friction, the company seeks to optimize OTC drug formulation by targeting well-established yet underutilized ingredients.
90 Mins
Reduced Screening Time ↓
24x Faster
Increased Screening Speed↑
Industry
Life Sciences (Pharmaceutical Manufacturing)
Business Problem
- Broken Data Plumbing: Teams had to manually extract and cross-verify ingredient information across multiple unconnected sources, such as: FDA, the FDA Orange Book, clinical trial data, trademark repositories, and market activity trackers, leading to lengthy and error-prone research cycles.
- Time-Intensive Ingredient Identification: The absence of intelligent automation resulted in R&D teams spending 3–4 months manually validating potential drug ingredients across regulatory, clinical, and market datasets, creating bottlenecks in screening speed and stalling product pipeline growth.
- Missed OTC Opportunities:Without proactive identification of “orphan” drugs (ingredients with limited market presence), the company struggled to identify underutilized, low-competition ingredients, resulting in missed chances to rapidly commercialize unique, high-value OTC products with minimal market competition.
- High Risk of Trademark Conflicts: Manual verification processes to check ingredient-related trademark availability were cumbersome and increased the risk of conflicts at late stages of product commercialization.
Solution Approach
- Unified AI-driven Ingredient Intelligence Platform: We engineered a robust platform that integrated data from sources such as: FDA, Orange Book, DrugBank, USPTO trademark databases, and other regulatory sources, providing a single point of truth for ingredient research and selection.
- Off-Label & OTC Classification AI Model: Developed and deployed AI-powered classification models that automatically distinguish between prescription and OTC drugs, including off-label use indicators, enabling rapid decision-making on formulation pathways.
- Automated Orphan Ingredient Discovery: Created automated pipelines connecting Analysource and market sales datasets to instantly identify orphan ingredients, uncovering low-competition, high-opportunity candidates, and accelerating their evaluation for rapid entry into the OTC drug market.
- Trademark Conflict Detection: Deployed automated USPTO data pipelines that continuously monitor for trademark ownership conflicts, empowering early-stage identification of legal risks, preventing costly product reworks, and safeguarding commercialization processes from late-stage disruptions.
Our Case Study
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