Turning Clinical Data into Confident Development Decisions
IQA's Clinical & Quantitative Pharmacology team applies model-informed drug development (MIDD) to de-risk dosing decisions, accelerate regulatory strategy, and strengthen the scientific foundation of every clinical program we support.
How We Think About Pharmacometrics
At IQA, Clinical & Quantitative Pharmacology (CQP) sits at the intersection of clinical science and quantitative modeling. We help sponsors answer the questions that shape a program's trajectory - What dose? What population? What's the evidence for regulators? - using rigorous, model-informed approaches rather than guesswork.
Whether you're preparing for a first-in-human study, optimizing dose selection for a pivotal trial, or building the quantitative evidence package for submission, our team brings the scientific depth and regulatory fluency to move your program forward with confidence.
Core Elements of Our Approach
Six capabilities spanning early-phase modeling through regulatory submission
Population PK/PD Modeling
- Characterize how your drug behaves across diverse patient populations, informing dose selection and identifying key covariates that matter for safety and efficacy.
Exposure-Response Analysis
- Link drug exposure to clinical outcomes to support dose justification, label language, and go/no-go decisions with defensible, data-driven evidence.
Model-Informed Drug Development (MIDD) Consulting
- Integrate quantitative modeling into your development strategy from early phase through submission - reducing late-stage surprises and strengthening regulatory dialogue.
Clinical Trial Simulation
- Test trial designs virtually before committing resources, optimizing sample size, dosing regimens, and study duration.
Dose Optimization
- Identify the dose that balances efficacy and safety, backed by quantitative rationale regulators expect to see.
Regulatory & Submission Support
- Translate complex modeling outputs into clear, submission-ready evidence packages aligned with FDA and EMA expectations.
From raw data to regulatory dialogue — a rigorous, iterative process
Raw data - Collect and clean
PK, PD, and clinical data are gathered, curated, and quality-checked before modeling begins.
EDA - Explore data
Explore data visually and statistically to understand distributions, trends, and anomalies.
Covariates - Key drivers
Identify patient or study factors (weight, renal function, formulation, etc.) that influence exposure or response.
Build model - PK/PD structure
Construct a population PK, PK/PD, or PBPK model that captures pharmacology and supports decision-making.
Validation - Check fit
Check predictive performance, diagnostics, and robustness to ensure reliable outputs.
Simulation - Test scenarios
Use the validated model to simulate alternative doses, regimens, or trial designs.
Decision - Dose and design
Translate simulation outputs into dose selection, population targeting, or trial design recommendations.
Regulatory - Dialogue and file
Package findings into submission-ready evidence to support regulatory interactions.
Four reasons sponsors trust us with their quantitative pharmacology strategy
Scientific Depth, Practical Delivery
Our team combines academic rigor with CRO-speed delivery.
Regulatory Fluency
We build every analysis with submission-readiness in mind, aligned to FDA and EMA expectations from day one.
Validated, Audit-Ready Infrastructure
Our modeling environment (NONMEM, R) is documented and validated to regulatory standards, so your submission stands up to scrutiny.
A Partner, Not Just a Vendor
We integrate early - ideally at protocol design - so quantitative thinking shapes your program from the start, not just the data analysis at the end.
Frequently Asked Questions
Ready to strengthen the quantitative foundation of your program?
Schedule a conversation with our team to discuss how model-informed approaches can support your next milestone.