Section (A): What are Synthetic Respondents

While most use cases of Generative AI in Market research have limited themselves to helping speed up an existing process, there is 1 emerging trend that stands out to redefine how MR is conducted, across industries. Generative AI has made it possible to simulate “synthetic respondents” - virtual HCPs, patients, caregivers, or payers that can participate in surveys and interviews on demand. The promise is compelling: faster insights, lower costs, and scale without the traditional constraints of recruitment.

A few months ago, driven by strong client interest in this area, we began exploring the landscape of synthetic respondents. What we found was a spectrum of emerging offerings - ranging from custom-built solutions to ready-made, off-the-shelf panels - often accompanied by bold promises, which looked too good to be true. We took the opportunity to build smaller steps that help solve use cases while maintaining client trust in the development process. As we ran experiments training and testing a variety of different LLMs, the conclusion became clear: while synthetic respondents show real promise, the reality is far more nuanced than what hyped-up marketing suggests.

Synthetic respondents can be powerful, but only when grounded in substantial past research data, applied thoughtfully, and directed at the right types of questions and used to supplement, not replace real respondents.

Synthetic respondents are not oracles - they are pattern generators. Their value depends on the quality and relevance of the data used to shape them. Without grounding in curated, domain-specific evidence, they risk producing generic, polished but shallow outputs that mislead rather than inform. 

Be anchored in a body of historical research 

- qualitative transcripts, survey microdata, Voice of Customer insights, medical publications, and real-world evidence, often tailored for nuances of a particular therapy area.

Reflect meaningful variation in opinions of the stakeholders

 - mirroring the diversity of markets (e.g., high vs. low prescribers, US vs. EU5, safety seekers vs efficacy proponents).

 - benchmarked against hold-out samples or small-scale human reads to ensure they align with reality.

- to capture changes in guidelines, emerging therapies, or market sentiment.

Without this foundation, synthetic respondents are little more than general-purpose LLMs “speaking in character.”

At ProcDNA, we take a disciplined approach. We don’t promote “instant AI panels.” Instead, we help clients build synthetic respondents from their own datawhile integrating our vast understanding of the pharmaceutical market and the research questions that come with it, ensuring:

 trained on therapeutic-area-specific evidence, reflecting current realities.

 structured validation against past research and hold-out samples.

 designed to mirror real-world diversity, not just averages.

 human-in-the-loop processes to decide where synthetic insights suffice and where real respondents are essential.

The outcome is a trusted tool for incremental learning, reducing cycles and costs while safeguarding quality.

Synthetic respondents should be seen as amplifiers, not replacements. They can compress timelines, reduce waste, and sharpen focus when the task is incremental and well-bounded. But they cannot substitute for human depth, emotional nuance, or the ability to anticipate unprecedented change.

 - to prune options, refine instruments, and test hypotheses.

- to capture authenticity, emotion, and confirm decision-critical insights.

This balance ensures that organizations benefit from efficiency without losing the richness of real voices.

In talking to many of our clients in insights and marketing teams, we explored where synthetic respondents can fit in different therapy areas to solve for specific unresolved challenges of today.

Based on these learnings, we are presenting clear pathways for impact that synthetic respondents can create through “Where do I call them” guide.

Section C : Concept to Scale : our playbook for leveraging synthetic respondents

It’s critical to take an early pragmatic approach to assess if synthetic respondents can add value to a project or program.

At ProcDNA, we are writing an industry first playbook on how to devise and deploy your strategy on synthetic respondents, anchored on clarity, governance, and measurable outcomes.

Consideration 1: What Synthetic Respondents Can and Can’t Do

 highlighting confusing wording, identifying missing probes, and improving flow before expensive fieldwork. Your first draft looks more compelling and fielding ready.

 running dry-runs of survey instruments or dashboards to identify logic gaps before field deployment.

providing quick directional input to narrow options, saving time and cost before validating with real respondents.

stress-testing changes like dosing frequency, administration routes, or packaging - when the broader clinical paradigm is already familiar.

 simulating attitudinal clusters to inform more robust real-world segmentation studies.

Radical innovations such as first-in-class therapies, new mechanisms of action, or paradigm-shifting treatment models.

Emotionally or ethically complex topics where authenticity and lived experience drive insight and potential action for brand or marketing teams.

High-stakes quantitative decisions (e.g., pricing, forecasting, access strategies) without human corroboration.

Consideration 2: Steps involved in planning and execution

A phased approach ensures credibility and organizational buy-in along with long term success:


Select domains with deep historical data (oncology, cardiometabolic disorders, immunology) and evident gaps which synthetic respondents can help fill


Apply synthetic respondents to small, well-bounded questions (survey flow testing, early message screeners). Refer to our “Where do I call them” guide for initial ideas


Compare outputs against small-scale human reads to benchmark accuracy. This may also include training your model on a subset of synthetic respondents, and measuring accuracy of predicted respondents from the remaining of the hold out sample at an aggregate level


Formalize human-in-the-loop processes and expand synthetic respondent use to cross-market research programs. Make sure the global research compliance and reporting requirements are met as we scale these globally. Document results for compliance and audit purposes

While it may seem tricky to track success of a program which replies on “synthetic replies”, but like any other AI modelling, and MLOps, there are clear ways to measure performance, helping enhance it over time and add more value in subsequent deployments

 – weeks or months saved in insights generation

 – % savings in recruitment and fieldwork spend

 – ability to simulate under-represented geographies or rare subpopulations

– degree of alignment with human-derived insights

The future that shall embrace synthetic respondents

While today, the debate goes on for the utility and trust of synthetic respondents, we aren’t very far from the reality where these will become essential to how some market research is conducted. To prepare for this inevitable future, insights team should look to stay ahead of the curve and plan to embrace them as part of their toolkit. The future landscape quickly evolve on several critical parameters:

 With the ability to have an new claims analytics or market research insights feeding into a calibration engine for synthetic respondents, teams will have a “Persona repository” to select their target respondent population. Teams will be able to field the surveys and discussion guides to a large pool of agent respondents

 Many market research projects may benefit from sequential (for testing and refining) or parallel fielding (for bridging gaps, making sample more representative of the overall population)

 Strong internal governance of this process would keep us ahead of the curve, while regulation may come in specific areas and applications to make sure the insights generated or applied keep the human empathy and impact in mind

 With synthetic respondents bringing time saving in processes, speed to insights, cost efficiency and quality assurance, we will see a broader adoption across processes with quantified positive impact

Below is a sneak peak into the future we are building for this incredible evolution to market research:

Synthetic respondents can accelerate research - but only when used with discipline. The key is not to ask them to do everything, but to point them at the right problems: those that are incremental, evidence-rich, and suited to fast iteration.

At ProcDNA, we believe the future of insights is not AI versus humans, but AI with humans - each applied where they deliver the most value.

Curious how synthetic respondents can accelerate your pharma insights while maintaining rigor and relevance? Let’s explore how ProcDNA’s disciplined, hybrid approach can help you reduce cycles, sharpen decisions, and scale this exciting new capability responsibly. Our team of leaders - 

 - are at the forefront of leveraging synthetic respondents to fill data gaps, generate actionable insights, and drive smarter strategies.

 to discuss a tailored pilot or integration strategy for your organization.

Synthetic Respondents in Pharma: Opportunity to fill Gaps, within Guardrails 

— Target Product Profiles (TPPs) are vital for drug development but often suffer from inefficient management. Intelligent TPP leverages AI, automation, and data engineering to streamline workflows, enhance collaboration, and ensure consistency. This unified platform enables agile, data-driven decision-making, giving pharmaceutical teams a competitive edge.

GenAI, Target Product Profile, TPP, Benchmark, Competitive Intelligence, Drug Development, Clinical Data Management, Market Access, LLM, AWS.

In the rapidly evolving pharmaceutical landscape, competitive intelligence (CI) plays a crucial role in shaping Target Product Profiles (TPPs) by providing real-time insights into market trends, competitor strategies, and evolving benchmarks. TPPs serve as strategic blueprints that define key attributes, success criteria, and development goals for pharmaceutical products, ensuring cross-functional alignment. This is particularly vital in highly competitive therapeutic areas like oncology, where differentiation is critical to market success.

Benchmarking is central to effective TPP development, as companies must evaluate their product attributes against competitor offerings, standard-of-care treatments, and emerging innovations. Traditionally, this benchmarking process relies on fragmented, manual research—analyzing clinical trial results, regulatory filings, pricing strategies, and key opinion leader (KOL) insights. However, given the rapid pace of scientific advancements and regulatory changes, static benchmarks quickly become outdated, making traditional TPP management inefficient.

Competitive intelligence enables a dynamic benchmarking approach, ensuring TPPs remain aligned with evolving industry standards and market expectations. By continuously analyzing clinical efficacy, safety profiles, patient outcomes, pricing models, and market access conditions, CI allows pharmaceutical companies to refine TPPs in response to real-world shifts. This 

agility is crucial for identifying competitive gaps, optimizing target product differentiation, and ensuring regulatory and commercial viability.

The advent of Generative AI (GenAI) and advanced analytics has transformed how CI is gathered, analyzed, and applied to TPP development. These technologies automate data extraction, real-time benchmarking, and competitive landscape analysis, enabling companies to stay ahead of market dynamics.

This white paper introduces Intelligent TPP, an innovative solution that integrates competitive intelligence with AI-driven automation to streamline TPP lifecycle management. By leveraging GenAI, data engineering, and MLOps, Intelligent TPP synthesizes diverse data sources—clinical trials, regulatory pathways, commercial intelligence, and competitor insights—to provide dynamic, evidence-based benchmarks for TPP development. Key features include – end-to-end TPP development and management, role-based access controls, automated workflows, and AI-powered data extraction, all designed to enhance collaboration and decision-making.

With Intelligent TPP, pharmaceutical companies can transform TPP management into a proactive, intelligence-driven process, ensuring that their product strategies remain competitive, differentiated, and aligned with market expectations. By establishing a single source of truth, the platform empowers stakeholders to respond swiftly to industry changes, optimize product positioning, and maintain a competitive edge in the life sciences sector.

The process of developing and managing TPPs has long been fraught with inefficiencies and operational bottlenecks. Despite their critical importance in guiding product development and strategic decision-making, TPPs are often developed manually & managed using fragmented workflows. These outdated methods create challenges that not only slow down operations but also undermine the accuracy and reliability of TPP-related decisions.

Currently, TPP development is highly manual, requiring individuals to sift through vast amounts of literature and internal documents to extract relevant information, structure it into a document, and socialize it with stakeholders for feedback and finalization. This approach is slow, prone to errors, and inefficient, often leading to delays and inconsistencies. Without streamlined workflows or automated tools, collaboration becomes cumbersome, feedback cycles are prolonged, and critical details may be overlooked, making the process sub-optimal and misaligned with the dynamic needs of regulatory and market environments.

One of the most glaring issues is the lack of a centralized platform to manage TPPs, often leaving team members searching for the latest version due to the absence of a single source of truth. This disjointed approach wastes time, causes confusion, and delays critical updates needed to address market or regulatory changes. Additionally, the lack of clear ownership or governance processes makes it difficult to identify who is responsible for maintaining and updating TPPs, leading to accountability challenges and further inefficiencies in collaborative workflows.

Another major challenge is the lack of transparency around the rationale behind changes to TPPs. Teams are often left to guess why certain benchmarks or attributes have been updated, as there is no standardized way to document or communicate the reasons for changes. This lack of traceability hinders trust and alignment among cross-functional teams, making it difficult to form informed decisions or align on future updates.

These inefficiencies are particularly problematic in markets where rapid innovation and dynamic competition demand agility and precision. The inability to quickly locate, understand, and update TPPs results in lost opportunities, delayed decision-making, and an increased risk of misalignment across teams.

The need for a streamlined, intelligent solution is clear. A centralized collaborative platform is essential to address these inefficiencies, ensuring that TPPs are managed with greater transparency, accountability, and agility2. By eliminating the need for manual processes and enabling teams to access a single source of truth, such a solution would not only save time but also empower organizations to make faster, more informed decisions in the highly competitive life sciences landscape.

Figure2: Current TPP lifecycle management process is complex & manual.

The Intelligent TPP (Target Product Profile) solution is a groundbreaking platform designed to address the inefficiencies in TPP management by leveraging GenAI and automation. It offers a unified, end-to-end lifecycle management system for TPPs, enabling teams to collaborate effectively and make informed, data-driven decisions in a dynamic, fast-paced environment.

Key features of the Intelligent TPP solution include:

 Leverages commercially available AI models to extract TPP information from internal and external data sources such as PubMed, ClinicalTrials.gov, and organizational intelligence (SharePoint, Emails, Literature Repositories, etc.).

· Automated Version Control: Ensures a single source of truth for TPPs by automatically managing different versions, reducing manual tracking efforts.

 Facilitates seamless collaboration among stakeholders through role-based access control, real-time annotations, and streamlined approval workflows.

 Provides an intuitive, searchable repository of TPPs and Benchmarks, making it easy to locate the latest TPP versions or benchmark data.

 Ensures robust authentication and access control using SSO and AWS-native security measures. All data remains within the organization as it is securely stored in the organization’s cloud environment.

 Tracks changes to TPPs with detailed annotations, ensuring transparency in decision-making.

 A cloud-first application, built on AWS infrastructure - the platform supports a scalable, multi-availability zone deployment to ensure high availability and fault tolerance.

By transforming the traditional TPP management process, Intelligent TPP enables pharmaceutical organizations to reduce manual effort, improve collaboration, and maintain a competitive edge.

The Benchmarks Hub in the Intelligent TPP application acts as a centralized repository for creating, managing, and accessing clinical benchmarks and competitive intelligence that form the foundation of Target Product Profiles (TPPs). It allows users to define benchmarks with precision by leveraging multiple dimensions, such as therapeutic area (TA), geography, line of therapy, and clinical trial phase, enabling highly granular insights. The platform integrates seamlessly with diverse data sources—such as public repositories, internal documents, and manual inputs, where information is extracted using web scraping and advanced parsing techniques, processed by GenAI to organize and structure the data as needed, and then presented to the user in a clear, actionable format. This process ensures that benchmarks are both accurate and relevant, providing a strong foundation for strategic decision-making while developing TPPs for a product.

The homepage of the Benchmarks tab is designed to provide users with a high-level overview of all available benchmarks. The interface includes a search bar and advanced filtering options to enable users to locate specific benchmarks quickly. Filters include criteria such as disease area, benchmark source, benchmark type, line of therapy, geography, and phase, allowing users to refine the displayed data based on their needs.

The benchmark list is displayed in a structured table format, showing key metadata such as the benchmark name, source, study or research name, comparator arm, and status (e.g., approved or emerging). The "Add Benchmark" button is prominently located at the top-right corner of the screen, providing users with quick access to the benchmark creation workflow.

The benchmark creation workflow offers an intuitive, feature-rich process to define and source benchmarks tailored to user needs.

: Users can input critical details such as regimen name, disease area, indication, geography, clinical trial phase, and line of therapy (e.g., first-line or second-line treatment). Mandatory fields ensure essential information is captured before proceeding.

: Users can choose from multiple data sources:

: Extract data from public repositories like ClinicalTrials.gov or PubMed.

: Upload files or access SharePoint repositories.


: Directly enter data using a built-in form. Clear icons and descriptions simplify the selection process.

: For web-based extractions, users can search and filter studies (e.g., by NCT ID, phase, or results) with metadata like study name, status, interventions, and location. Selected studies can be directly used for data extraction.

: Users can review and modify attributes such as target patient population, efficacy, safety, dosing, and healthcare resource use. This ensures that only relevant information is extracted, aligning with specific benchmarking requirements.

The streamlined workflow combines flexibility, precision, and user-friendly design to deliver actionable benchmarks efficiently.


3. Key Features of the Benchmarks Hub
The application offers advanced search and filtering capabilities, enabling users to quickly locate benchmarks based on various criteria. It supports multi-source data integration, allowing data extraction from public repositories, internal documents, and manual input, ensuring flexibility across different use cases. Users can define the attributes they wish to extract, making the process highly customizable. Additionally, extracted benchmarks can be linked to TPPs or shared with collaborators for review and validation. The application also features an approval workflow, categorizing benchmarks as "Approved" or "Emerging," with a structured process for promoting emerging benchmarks to approved status.

By offering a comprehensive and flexible benchmark management system, the Benchmarks Hub complements the TPPs tab to provide a seamless, end-to-end solution for managing product development profiles. Its integration with multiple data sources and advanced customization options ensures that users can create high-quality benchmarks that drive informed decision-making.

The Intelligent TPP (Target Product Profile) solution is developed as a cutting-edge, cloud-based platform designed to streamline TPP lifecycle management. Built on AWS infrastructure, it employs a three-tier architecture—comprising a presentation layer, application layer, and data layer—that integrates advanced technologies such as Generative AI (GenAI), automated workflows, and robust security measures. This section provides an in-depth technical overview of the implementation, highlighting the intricacies and decisions that shaped the platform’s development.

The architecture of Intelligent TPP is designed to ensure scalability, reliability, and maintainability. The solution is based on a three-tier model:

1. Presentation Layer (Front-end): The user-facing interface, built with Next.js, is hosted on AWS Amplify. It provides a seamless experience for TPP creation, updates, and management.

2. Application Layer (Backend): This layer, hosted on AWS EC2 instances, handles business logic, API orchestration, and integration with external data sources and AI models.

Data Layer (Database): AWS RDS Postgres stores all structured data, such as TPP attributes, version histories, and user activities, while AWS S3 serves as an archival and logging solution.

The development of Intelligent TPP prioritized a user-centered design process, incorporating focus groups, expert UI/UX reviews, and iterative testing to refine the interface. Early prototypes were evaluated by UX specialists, ensuring accessibility, intuitive navigation, and seamless workflows. Insights from focus group sessions helped identify usability challenges, guiding improvements in layout, information hierarchy, and interaction design.

Multiple rounds of end-user testing were conducted, gathering real-world feedback to address pain points and enhance overall usability. The process followed an iterative design cycle, where refinements were continuously made based on testing outcomes, ensuring a smooth, efficient, and user-friendly experience for all stakeholders.

The front-end is hosted using AWS Amplify, which offers a fully managed service for deploying static and server-rendered web applications. Amplify is chosen for its serverless architecture, built-in CI/CD pipeline support, and ability to integrate with GitHub repositories. Each code update triggers an automated build and deployment, ensuring rapid iteration without manual intervention.

Amplify connects with AWS Route 53 for DNS management, providing a custom domain for the application. Secure communication is ensured using AWS Certificate Manager (ACM), which provides SSL/TLS certificates. By using server-side rendering capabilities of Next.js, the front-end provides a fast and interactive user experience. Features include dynamic search functionality, collaborative editing interfaces, and intuitive dashboards for tracking TPP updates.

The backend of the Intelligent TPP platform is implemented on AWS EC2 instances to handle the core logics of TPP lifecycle management. Docker containers are used to package and deploy the application, ensuring consistency across development, testing, and production environments. The backend supports a wide range of functionalities, including API processing, data integration, user authentication, and AI-based data extraction.

1. Compute Resources:
The backend application is hosted on EC2 instances within a Virtual Private Cloud (VPC). Each instance operates within a private subnet to ensure restricted access, while an Application Load Balancer (ALB) in a public subnet manages incoming traffic. For production environments, the backend is deployed across multiple Availability Zones (AZs) to ensure fault tolerance. The ALB performs SSL termination, offloading encryption and decryption tasks from the EC2 instances.

2. Auto Scaling and Traffic Management:
To handle fluctuating workloads, Auto Scaling Groups (ASG) dynamically adjust the number of EC2 instances based on predefined metrics such as CPU utilization and request volume. This ensures that the system scales up during high-traffic periods and scales down to save costs during low activity periods. The ALB evenly distributes incoming requests across available instances, ensuring optimal performance and preventing bottlenecks.

3. API Gateway and Integration:
The backend exposes REST APIs for communication with the front-end and third-party services. APIs handle core operations such as TPP creation, modification, and approval workflows. These endpoints also support data ingestion from external sources like PubMed and ClinicalTrials.gov, which are essential for generating benchmarks and updating TPPs.

Data storage is a critical component of the Intelligent TPP platform, ensuring reliability, scalability, and security.

1. Relational Database
The platform uses AWS RDS Postgres for managing structured data. RDS Postgres is selected for its support of JSON data types, which allows flexible storage of TPP attributes and metadata. In production environments, RDS is configured for Multi-AZ deployment to ensure high availability. A primary instance handles read and write operations, while a standby instance in another AZ is synchronized in real-time for failover purposes.

2. Archival and Logging
Historical data, including logs and version histories, is archived to AWS S3 for long-term retention. S3 offers robust durability and cost optimization through lifecycle policies, which transition data from S3 Standard to lower-cost tiers like S3 Standard-IA after two years. Archived logs are stored in JSON format, allowing easy retrieval and querying when needed.

3. Data Retrieval and Export
Users can query archived data through the platform’s interface by specifying parameters such as date range or TPP version. Retrieval processes are managed by backend API calls, which generate and store export files in S3. Notifications are sent to users when data exports are completed, enhancing usability.

One of the most innovative aspects of the Intelligent TPP platform is its use of Generative AI for data extraction. This capability streamlines the traditionally manual process of identifying and updating TPP attributes.

1. Generative AI Models
The platform employs AWS Bedrock to access Claude-3, a state-of-the-art large language model (LLM). While Claude-3 is chosen for its ability to handle vast token limits (up to 2 million) and its high accuracy1 in extracting attributes like safety endpoints and efficacy metrics, 

the platform is designed to be LLM-agnostic

, allowing for the integration of other LLMs as needed. Parameterized zero-shot prompting enables the model to identify specific data points from unstructured text, eliminating the need for extensive fine-tuning.

2. Data Pipelines
Data ingestion pipelines integrate with APIs from external sources, including but not limited to PubMed and ClinicalTrials.gov, to fetch relevant medical data. The raw data is cleansed and pre-processed before being passed to the LLM for attribute extraction. Outputs are validated and stored in the RDS database, enabling seamless updates to TPPs.

Robust security measures ensure the protection of user data and application infrastructure.

1. Authentication
SSO Management Service of choice handles user authentication, providing a centralized and secure login mechanism. SSO tokens are also used for accessing SharePoint data, which is integrated into the platform for document retrieval and collaboration.

2. Encryption and Key Management
Sensitive information, including database credentials and API keys, is encrypted using AWS Key Management Service (KMS). All data is encrypted at rest and in transit using industry-standard protocols, ensuring compliance with regulatory requirements.

3. Network Security
The application employs AWS Web Application Firewall (WAF) to protect against common exploits such as SQL injection and cross-site scripting. Security Groups within the VPC enforce strict inbound and outbound traffic rules, allowing only authorized access.

Comprehensive logging and monitoring capabilities provide visibility into system performance and user activity.

1. Logging
User interactions, API calls, and system events are logged and stored in AWS S3. Logs are also ingested into AWS CloudWatch, where administrators can set up alerts for anomalies such as high CPU usage or failed API requests.

2. Monitoring
CloudWatch monitors the health of backend services, including EC2 instances, ALB, and RDS databases. Metrics such as response times and error rates are visualized on dashboards, enabling proactive issue resolution. A dedicated admin interface allows platform administrators to view logs and track application usage.

The Intelligent TPP platform adopts modern DevOps practices to streamline development and deployment.

1. CI/CD Pipeline
The front-end CI/CD pipeline is managed by AWS Amplify, which automates the build and deployment process. For the backend, GitHub Actions triggers workflows that include code linting, unit testing, and integration testing. AWS CodeDeploy handles deployment to EC2 instances, ensuring a seamless rollout process.

Intelligent TPP 

The 2025 WCG Clinical Research Site Challenges Report highlights persistent difficulties in clinical trial execution, including limited physician availability, patient recruitment challenges, delays in study start-up, and increasing operational burdens at research sites. These constraints continue to affect enrollment timelines, site performance, and overall trial success.

Principal Investigators (PIs) play a pivotal role in mitigating these challenges; however, traditional PI selection remains manual, relationship-driven, and reliant on self-reported information. This paper outlines the data-driven methodology—powered by ProcDNA’s Principal Investigator Scoring Engine, which integrates multi-source data to objectively assess, score, and rank PIs based on experience, recruitment potential, and operational readiness.

1. Struggles Faced in Conducting Clinical Trials 

As per the survey, below are the dominant challenges affecting trial timelines, quality, and overall execution:

Reduced availability and declining willingness among physicians to serve as PIs due to administrative burden, increasing protocol complexity, and competing clinical demands.

Sites struggle to identify, pre-screen, and retain eligible patients, particularly under narrow inclusion/exclusion criteria and rising operational load

Budget negotiations, contracting, coverage analysis, IRB processes, and technology provisioning routinely extend activation timelines beyond industry expectations.

Persistent staffing shortages, turnover, and operational complexity limit the number of studies sites can manage and contribute to variability in performance.

These challenges heighten the importance of selecting strong PIs who bring patient access, operational discipline, and clinical expertise—factors that directly influence feasibility, enrollment reliability, and overall trial execution.

The selection of Principal Investigators (PIs) is a critical factor in the success of clinical trials. PIs remain central to trial performance across recruitment, data integrity, and compliance dimensions:

A strong patient base enables PIs to achieve enrollment rates

Experienced investigators enable consistent protocol adherence and reliable data capture.

PIs adhere to Good Clinical Practice (GCP) and FDA regulatory guidelines, reducing audit findings and operational risk.

3. Traditional PI Identification Method & its Limitations

Sponsors have historically relied on a small set of qualitative, relationship-driven steps to identify investigators:

Heavy reliance on known investigators, prior performance, and CRO recommendations.

Site-reported estimates of patient availability, staffing capacity, and anticipated enrollment.

High-level assessments of infrastructure, SOPs, staffing levels, and prior audit outcomes.

 -These practices form the backbone of traditional PI selection, but they do not fully address modern complexity, rising site burden, or the need for scalable, objective investigator identification. The legacy approach introduces several structural constraints:

Favoring familiar investigators restricts access to diverse, emerging, or high-potential PIs.

Self-reported metrics lack triangulation with real-world evidence, reducing enrollment predictability.

Manual spreadsheets and disconnected data provide limited visibility into workload, multi-sponsor performance, or phase-level experience.

These limitations heighten the risk of slow enrollment, delayed activation, and inconsistent site performance—issues already magnified by findings in the survey.

4. ProcDNA’s Data-Driven Approach for Effective and Smarter PI Identification

To address these gaps, sponsors need an evidence-driven, multi-source, and scalable method to objectively identify the most suitable investigators—both established and emerging.

ProcDNA’s methodology directly supports this requirement. Our PI identification framework builds a structured, comprehensive, and objective evaluation of investigators that strengthens feasibility, accelerates enrollment, and improves trial outcomes. The methodology involves four key steps:

Create a comprehensive investigator universe by first building the base PI list using claims, prescription, and CMS Open Payments data. Then enrich it with key PI attributes—including patient demographics, prescribing patterns, clinical trial history, geographic footprint, site characteristics, and institutional affiliations—sourced from these datasets and additional inputs such as affiliation databases, Citeline/CRO sources, NIAID trial datasets, social media intelligence, etc.

4.2 Develop a Comprehensive PI Profile using Multi-Source Data

Each PI profile integrates structured attributes from:

PI demographics gathered from Affiliations data:

 Include PI specialty, practice location, and years of experience.

Patient potential derived from Claims data:

 Evaluate patient demographics, prescribing habits, treatment history

Clinical value based on Citeline/CRO/NIAID trial data:

 Utilize PI data from previous and current clinical trials, including phases, enrollment levels, success rates, and current trial load to gauge investigator skill, site effectiveness, and trial experience.

Academic affinity drawn from Research Publications and CMS Open Payments data:

 Include affiliations, publication records, and funding history to assess academic contributions.

Social media influence from web-scraped data

: Analyze follower counts, posts, tweets, and social media presence to measure engagement and influence.

                      Example of Comprehensive PI Profile View (1/2)

                     Example of Comprehensive PI Profile View (2/2)

4.3 Run ProcDNA’s Principal Investigator Scoring Engine to algorithmically rank and score PIs

Before running the Scoring Engine, first filter for relevant PIs by reviewing foundational attributes - geography, specialty alignment, and years of clinical experience -to ensure feasibility and therapeutic‑area fit. In parallel, apply demographic filters such as age and gender on patient data to ensure that the PI’s patient population aligns with trial eligibility.

The Scoring Engine generates a composite Principal Investigator Index (PII) by applying weighted metrics across four dimensions: patient potential, clinical value, academic affinity, and social affinity. The weighting hierarchy is patient potential > clinical value > academic affinity > social affinity. By default, industry -standard weights are used: 0.4, 0.3, 0.2, and 0.1, respectively. 

Across these four dimensions, the following KPIs are used to evaluate and rank PIs:

 Evaluate total diagnosed and treated patients, and prescribing patterns that signal treatment preferences and recruitment potential.

 Evaluates a PI’s trial performance and operational strength based on trial type, patient enrollment volume, phase experience, trial status, success rate, current trial load, and prior relationships with the sponsor.

 Measures scientific credibility through publication influence, citation strength, research funding, and academic affiliations that indicate research capability and institutional support.

 Assesses professional visibility and influence by analyzing follower base, posting activity, audience engagement, and overall digital footprint across medical and scientific platforms.

Example of real-world simulation for potential PI, scored using ProcDNA’s PI scoring engine

ProcDNA’s Principal Investigator - Scoring Engine output helps to easily identify the PIs with the highest relevance for our clinical trial

 of top investigators aligned with trial needs. The top-ranked PIs have a proven track record of successful clinical trials, high patient recruitment numbers, and significant research contributions, which will ultimately lead to efficient 

ProcDNA’s data-driven Principal Investigator (PI) identification methodology offers 

a scalable and objective alternative to traditional, relationship-driven selection.

 By integrating diverse real-world data sources - claims, Rx, CMS Open Payments, affiliations, and clinical trial intelligence—into a unified profiling and scoring framework, sponsors can reduce network bias, improve feasibility confidence, expand access to high-potential and emerging investigators, and strengthen enrollment predictability. This approach helps trial teams proactively identify investigators with the right combination of patient access, trial execution capability, and scientific credibility, ultimately lowering operational risk and improving the likelihood of on-time, high-quality trial delivery.

Building on recent evidence that machine-learning approaches can improve site ranking by predicting site-level patient enrollment using a combination of indication-level historical recruitment and real-world patient data, ProcDNA’s PI Identification framework can evolve from a primarily descriptive scoring model into a more protocol-specific, predictive engine -forecasting expected enrollment volume and enrollment speed for each PI/site under a given set of inclusion/exclusion criteria, and continuously refining rankings as new trial and real-world signals emerge. This would strengthen feasibility confidence upfront, reduce reliance on static assumptions, and enable sponsors/CROs to prioritize investigators who are not only high-scoring in general, but most likely to deliver recruitment targets for the specific study design and indication.

https://www.wcgclinical.com/wp-content/uploads/2025/10/siteChallengesReport25__final.pdf

https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0300109

https://purplelab.com/wp-content/uploads/2024/12/FINAL-Clinical-Playbook.pdf

https://milo-healthcare.com/en/site-identification-clinical-trial-experts/