Pharma Quick Start
Deploy your semantic knowledge graph faster with gistPharma—the knowledge graph accelerator built for pharmaceutical and clinical use cases
4 Reasons to Use gistPharma
Integration of Disparate Data Sources
R&D, clinical, and manufacturing data often reside in siloed systems; LIMS, ELNs, clinical trial databases, quality systems, and external data feeds.
A semantic knowledge graph provides a unifying model (ontology-based) that harmonizes these data silos, making them interoperable and discoverable across functions. This dramatically reduces time spent on manual data reconciliation and provides the basis for programmatically exploring a large number of data sources.
Accelerating Drug Discovery & Repurposing
By connecting chemical, biological, clinical, and real-world evidence data in a semantically rich graph, researchers can identify hidden relationships; such as novel biomarkers, off-target effects, or drug repurposing opportunities. Without a contextualized knowledge graph to expose the vital intersections, it is difficult and manually time consuming to detect intersections from isolated datasets.
This speeds time to decisioning based on interconnected visibility while maturing FAIR principle realization.
Enabling Advanced Analytics & AI/ML
Machine learning models in pharma often fail due to poor data quality, inconsistent metadata, or incompatible formats.
A semantic knowledge graph enforces consistent meaning across datasets, providing a “single source of truth” that improves training data quality and model explainability; critical for AI-driven clinical trial design, patient stratification, and adverse event prediction.
Regulatory Compliance & Data Traceability
Pharma companies face a variety of strict regulations across the pharmaceutical development pipeline requiring complete traceability of data across drug discovery, clinical trials, manufacturing, and post-market surveillance. These regulations are enforced in multiple jurisdictions by a variety of regulatory bodies (e.g., FDA, EMA, ICH).
A semantic knowledge graph links data across systems, enabling audit-ready traceability, faster regulatory submissions (eCTD), and reduced risk of non-compliance penalties.
About gistPharma
You can use the gistPharma ontology model to structure your information in:
- Pharmaceutical R&D
- Chemistry Quality Control
- Manufacturing
- Clinical Trials
- Regulatory Compliance
gistPharma is a performance enhancer.
It provides the foundation for your semantic knowledge graph that unifies diverse models and datasets within a hub-and-spoke, data-centric architecture.
With gistPharma, your data architecture is modular and harmonized, seamlessly orchestrating systems so their data plays well together.
Why we created gistPharma
The life sciences and pharmaceutical industry was an early adopter of semantic technologies, but adoption raced ahead of standardization and operational maturity. Ontologies now span a spectrum; from overly narrow controlled vocabularies to abstractions too vague for practical use. This fragmentation drives up integration costs, blocks interoperability, and leaves much of the original promise unrealized.
To close this gap, we developed an ontological model that serves as the backbone for simple yet powerful semantic knowledge graphs; capable of integrating heterogeneous data formats, standards, and domains across the entire pharmaceutical R&D pipeline. This approach allows us to deliver value to our clients faster, at lower cost, and with higher quality; backed by Semantic Arts’ 25 years of focused expertise in semantic modeling.
gistPharma is built for interoperability
If you are developing a knowledge graph project in your organization, you know how difficult it can be to determine where to start.
The choices can feel endless; ontologies, datasets, and vendors all promising the stars and the moon. To make matters worse, not all ontologies are created equal. Some ontologies merely represent domain vocabularies, others are logically incompatible with different ontologies, and many are not natively built on W3C open standards.
Drawing on projects undertaken in biotechnology, clinical research, and healthcare, we have developed a flexible and interoperable mid-level ontology that complies with standards such as BFO, CDISC, Allotrope, IDMP-O, and HL7 FHIR. Better yet, gistPharma fits seamlessly into your existing landscape while future-proofing your data, without vendor lock-in.
Start your projects with gistPharma
Serve Your Patients Better
You’re tasked to serve your patients better through your science, but your scientific endeavors are never reaching the patients as quickly or as effectively as they might due to the limitations of your information technology and the ways that information is organized within your enterprise.
There is a better way to provide lasting value that both improves patient outcomes and creates reusable knowledge assets that speed up subsequent developments. Rather than working within a fractured data landscape, you can leverage gistPharma to heal your fractured data.
Stop Paying the Price of a Fractured Data Landscape
You’re tasked with generating profit and enabling the business, but you’re constantly fighting a defensive battle against integration debt. Your teams are taxed with endless data reconciliation, introducing human error and burning budget on non-value-add activities.
This is why roadmaps are delayed, initiatives fail, and your most valuable human resources are frustrated. The problem isn’t the ambition of your goals; it’s the brittle foundation they’re built on.
A Non-Disruptive, Accelerated Path Forward
Adopting a Semantic Knowledge Graph capabilities is the strategic answer to eliminating integration debt. But starting this journey doesn’t require a disruptive, multi-year overhaul to realize value.
gistPharma is a purpose-built accelerator that provides a robust, enterprise-ready foundation for your data-centric future, starting in as little as 90 days. It allows you to bypass the costly, time-consuming foundational work and jump-start the development of scalable and future-proof use cases in regulated domains.
Disease Target Identification 
Discovery 
Preclinical Research 
Manufacturing & Controls 
Clinical Research 
Approval for Therapy 
Regulatory Submissions
Contact Us
Semantic Arts using gistPharma will deliver a scalable foundation for even your toughest use cases in just 90 days.
To get started, choose one of the following use cases that aligns with your organizational roadmap, and schedule a 15-minute strategy call to discuss the next steps.
Where are you in the Pharma use case tree?
1- Drug Discovery
- Target Identification and Validation: Create a unified view connecting genes, proteins, disease pathways, chemical compounds, and scientific literature. The knowledge graph can infer novel relationships between biological targets and diseases, suggesting new avenues for research that are not obvious from siloed data.
- Drug Repurposing: Link existing approved drugs, their known mechanisms of action, molecular structures, and reported side effects to a vast network of different diseases and biological pathways. This helps identify opportunities to repurpose existing safe compounds for new therapeutic indications.
- Competitor and KOL Intelligence Mapping: Map the landscape of Key Opinion Leaders (KOLs), research institutions, patents, publications, and ongoing clinical trials. The graph can reveal emerging research trends, identify key influencers in a specific therapeutic area, and track competitor R&D strategies in real-time.
2- Drug Development
- CMC Process Digital Twin: Model the entire Chemistry, Manufacturing, and Controls (CMC) process by linking raw material batches, supplier information, equipment parameters, process steps, and final product quality attributes. This enables root-cause analysis for batch failures and predicts the impact of process changes.
- Formulation Knowledge Management: Connect data on active pharmaceutical ingredients (APIs), excipients, solvents, stability study results, and manufacturing methods. This creates a corporate memory that prevents repeating failed formulation experiments and accelerates the development of stable, effective drug products.
- Supply Chain Traceability and Lineage: Build a complete, auditable lineage for every drug batch, tracing each component from its source vendor, through every manufacturing step, to the final packaged product. This is critical for quality control, recall management, and combating counterfeiting.
3- Pre-Clinical Research
- Cross-Study Toxicology Analysis: Integrate and harmonize data from multiple toxicology studies (in-vitro and in-vivo) across different compounds and animal models. The network of data can identify common toxicity signatures and link adverse outcomes to specific chemical structures or biological mechanisms, improving safety predictions.
- Unified 360° View of Research Molecules: Create a single, comprehensive profile for every candidate molecule by connecting its chemical structure, synthesis route, all experimental results (e.g., potency, selectivity, PK/PD), and associated internal study reports into one queryable resource.
- Pharmacokinetics/Pharmacodynamics (PK/PD) Data Aggregation: Link dosing information, sample collection times, biomarker measurements, and physiological responses from various pre-clinical models. This provides a holistic dataset for PK/PD modelers, enabling more accurate predictions of a drug’s behavior in humans.
4- Clinical Research
- Patient Cohort Identification for Trials: Connect complex clinical trial inclusion/exclusion criteria with a deep network of real-world patient data, including Electronic Health Records (EHR), genomic data, and lab results. This dramatically accelerates patient recruitment by finding qualified candidates who are often missed by traditional methods.
- Clinical Trial Site Selection: Link trial protocol requirements with data on investigator expertise (via publications), historical site performance, patient population demographics, and competing trials. The graph helps identify the optimal sites likely to enroll patients quickly and produce high-quality data.
- Adverse Event Causal Analysis: Model the complex relationships between reported adverse events, patient demographics, comorbidities, concomitant medications (polypharmacy), and biomarkers. The graph can help infer potential causal links and distinguish drug effects from confounding factors.
5- Regulatory Submissions
- Automated Regulatory Document Assembly: Link all data, figures, and text fragments from disparate source reports (CMC, pre-clinical, clinical) to their required locations in an electronic Common Technical Document (eCTD) submission. The network of data ensures consistency, provides traceability, and automates much of the assembly process.
- End-to-End Data Lineage for Audits: Create a transparent, auditable trail from a specific number in a regulatory submission summary all the way back to the raw instrument data or clinical observation it was derived from. Allowing for rapid response to regulator queries.
- ISO IDMP/SPOR Compliance Automation: Model all internal data on substances, products, organizations, and referentials according to the ISO IDMP standards. The network of data serves as a central, compliant repository that can be used to automatically generate regulatory messages and ensure consistency with global standards.
6- Post-Marketing Monitoring
- Real-World Evidence Safety Signal Detection: Integrate structured data from official adverse event databases (e.g., FAERS) with unstructured text from social media, patient forums, and EHR notes. The network of data can detect emerging safety signals and patterns of off-label use much earlier than traditional methods.
- Drug-Drug Interaction (DDI) Discovery: Analyze large-scale, real-world data on concomitant medications prescribed to patients and their reported health outcomes. This helps identify previously unknown or under-reported drug-drug interactions that were not detected in controlled clinical trials.
- Patient Treatment Pathway Analysis: Map real-world patient journeys by connecting prescriptions, diagnoses, procedures, and patient-reported outcomes from claims data and EHRs. This provides invaluable insight into how a drug is actually used post-approval and where it fits within the standard of care.
An Enterprise-Ready Foundation, Built for Your World
Build a strategic asset designed to de-risk your semantic journey and deliver immediate value:
- Rapid Time-to-Value: Our ontology was designed from the onset to accelerate use cases related to CMC and Clinical Trials, providing a robust starting point and cutting time-to-value from years to a single quarter.
- Seamless Integration & Future-Proofing: Natively compliant with industry standards like BFO and designed for interoperability with Allotrope, CDISC, IDMP-O, and FHIR, gistPharma fits into your existing landscape and prepares you for the future without vendor lock-in.
- Non-Disruptive Transformation: We enable effective change, without ripping out or replacing existing IT infrastructure. Our tailored architecture works alongside your current systems, ensuring a smooth, low-risk adoption process.
How are Pharmaceutical Companies using Semantic Knowledge Graphs?
Your Partner in Data-Centric Transformation
- 25 Years of Proven Experience: Benefit from a quarter-century of pioneering experience gained across more than 100 successful data integration projects in multiple industries. We’ve solved these problems before.
- Domain-Specific Pharmaceutical Expertise: From design to implementation, we bridge the gap between semantic technology and the rigorous demands of pharma development and compliance, ensuring your solution is economic, effective, and efficient.
- An Expansible, Non-Disruptive Solution: we deliver a complete, turnkey foundation for your digital transformation that works with your existing IT infrastructure, creating reusable knowledge assets without costly rip-and-replace projects.
Testimonials, as authentic endorsements from satisfied customers, serve as potent social proof, significantly inspiring trust in potential consumers.
Testimonials, as authentic endorsements from satisfied customers, serve as potent social proof, significantly inspiring trust in potential consumers.
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