PBPK Modeling of Metformin Dosing in Kids vs Adults

ISEF Category: Computational Biology and Bioinformatics

Subcategory: Computational Pharmacology  ·  Difficulty: Advanced  ·  Setup: University Lab  ·  Time: Full Year

The Hook

A dose that works for an adult can behave very differently in a child. The same milligrams per kilogram rule can miss how the body changes with age. That is why pediatric drug modeling matters. You can test whether common scaling rules match real metformin data.

What Is It?

PBPK modeling means building a computer model of how a drug moves through the body. PBPK stands for physiologically based pharmacokinetic modeling. Instead of treating the body like one simple bucket, the model splits it into organs, blood flow, and tissue spaces. That lets you ask how metformin travels through a child body versus an adult body.

Think of it like a city map with roads, traffic lights, and different neighborhood sizes. A small child is not just a smaller adult. Organ sizes, blood flow, enzyme activity, and kidney function can all shift the final drug exposure. Your project asks whether public allometric assumptions, which scale dose by body size, explain differences in adverse events well enough or whether age-specific physiology gives a better answer.

Why This Is a Good Topic

This is a strong science fair topic because you can ask a real, testable question with public tools and public data. You can compare model predictions against published pediatric and adult pharmacokinetic data, then check whether simple scaling rules match the data or fail in a predictable way. The project connects to drug safety, pediatric medicine, and fair dosing. You can also learn model building, data cleaning, parameter checking, and statistical comparison.

Research Questions

• How does PBPK-predicted metformin exposure differ between pediatric and adult virtual patients at matched allometric doses?
• What is the effect of using body-weight scaling versus age-specific physiology on predicted metformin clearance?
• Does adding pediatric kidney function parameters improve agreement between model predictions and published concentration data?
• To what extent do public allometric assumptions explain reported adverse-event differences between children and adults taking metformin?
• Which model inputs, body weight, glomerular filtration rate, or organ blood flow, most strongly change predicted metformin exposure?
• How does simulated peak concentration change across pediatric age groups when the dose stays fixed by body weight?

Basic Materials

• A laptop or desktop computer with enough memory to run PK-Sim or Open Systems Pharmacology tools.
• Open Systems Pharmacology Suite, including PK-Sim and MoBi.
• Spreadsheet software for cleaning data and tracking model inputs.
• PubMed access for finding pediatric and adult metformin studies.
• NIH or FDA drug labels for metformin dosing and safety background.
• A notebook for documenting assumptions, parameter sources, and model versions.
• A reference manager such as Zotero for organizing papers.

Advanced Materials

• A laptop or desktop computer with high RAM for larger simulation batches.
• Open Systems Pharmacology Suite, including PK-Sim and MoBi.
• A scripting environment such as R or Python for batch simulations and sensitivity analysis.
• PubMed datasets or extracted concentration-time data from pediatric and adult metformin studies.
• Clinical trial reports or pharmacokinetic study supplements with age-stratified parameters.
• A version control platform such as Git for tracking model changes.
• A statistics package such as R for model fit metrics, bootstrapping, and residual analysis.

Software & Tools

• Open Systems Pharmacology Suite: Builds and runs PBPK models in PK-Sim and MoBi for age-specific drug simulation.
• PK-Sim: Lets you set up virtual pediatric and adult populations and compare predicted exposure.
• R: Helps you compare model outputs, run summary statistics, and make publication-style plots.
• Python: Useful for batch processing results and automating sensitivity checks.
• Zotero: Organizes papers, PDFs, and citations for the background and methods section.

Experiment Steps

1. Define the exact comparison you want to test, such as pediatric versus adult exposure under the same public dosing rule.
2. Gather published metformin pharmacokinetic studies, then extract the age groups, dose rules, and outcome measures you can compare to model outputs.
3. Build two virtual populations in PK-Sim, one pediatric and one adult, and document every physiological assumption you change between them.
4. Choose the main model readouts, such as AUC, Cmax, and clearance, so you can compare prediction quality with one consistent metric set.
5. Plan a calibration strategy that uses one subset of literature data and a validation strategy that holds back separate studies.
6. Design sensitivity tests to see which assumptions, body size, kidney function, or absorption settings, drive the biggest change in predictions.

Common Pitfalls

• Mixing pediatric and adult papers with different sampling schedules, which makes the model look wrong when the data are not comparable.
• Treating allometric scaling as if it automatically captures kidney maturation, which can hide a real age effect.
• Using one published dose rule without checking whether the study reported fasting state, formulation, or route differences.
• Comparing simulated concentration curves to raw values instead of matching the same summary metric, which gives misleading fit results.
• Changing too many model parameters at once, which makes it hard to tell which assumption actually explains the exposure gap.

What Makes This Competitive

A stronger project does more than run one comparison. You can test several scaling assumptions, separate calibration from validation, and ask which physiological parameter matters most. A competitive entry often combines model fit metrics, sensitivity analysis, and a clear explanation of where simple allometry breaks down. If you compare age bands, formulations, or renal function assumptions, your work will feel much more like real pharmacology research.

Project Variations

• Compare immediate-release and extended-release metformin in pediatric versus adult virtual patients to see whether formulation changes alter the age gap.
• Swap metformin for another renally cleared drug with public pediatric data, then test whether the same scaling pattern holds.
• Focus on one pediatric age band, such as school-age children or adolescents, and compare multiple renal maturation assumptions.

Learn More

• Open Systems Pharmacology Documentation: Read the PK-Sim and MoBi guides, examples, and parameter notes on the Open Systems Pharmacology website.
• PubMed: Search for review articles on pediatric PBPK modeling, metformin pharmacokinetics, and allometric scaling.
• NIH LiverTox and drug information pages: Check general safety context and metabolism background for metformin on NIH sites.
• FDA Drug Label Database: Find official metformin labeling, dosing language, and safety warnings from the U.S. Food and Drug Administration.
• MIT OpenCourseWare Pharmacokinetics material: Review free lecture notes and course content on drug absorption, distribution, and clearance.
• R Project Documentation: Use the free R manuals and package vignettes for plotting, model comparison, and sensitivity analysis.