DVA_Data_Model

NONMEM Model DVA/PRR

Collaborative project with Merck, USTTB and Hamburg University

🔎 Objective

This pharmacodynamic (PD) model was developed to simulate the dynamics of parasite viability (DVA) and parasite reduction ratio (PRR) in response to various antimalarials (DHA, CQ, ATO, PYRI), using data from in vitro experiments. It implements an ON/OFF effect based on a LAGTIME to simulate the delayed drug response.

📂 File Structure

• DVA_PRR.csv: input data file containing DVA and PRR observations.
• runXXX.mod: NONMEM model file.

The dataset (.csv) and the model file (.mod) must be placed in the same folder. All output files generated by NONMEM (e.g., sdtab001, patab001) will also be saved in this same folder

🧰 Required Tools

To run this model, you will need:

• NONMEM 7.5
• PsN (optional but recommended for managing runs)
• Pirana (recommended for managing and visualizing model runs)
• R or Python (recommended for post-processing, graphics, NPDE, etc.)
• A text/code editor: RStudio, VS Code, or even Notepad++

🧾 Data Column Description

The data file must contain the following columns:

Column	Description
ID	Unique experiment identifier
TIME	Time after the start of the experiment (h)
AMT	Dose added to the well
CMT	PD compartment (1 = DVA, 2 = PRR)
EVID	NONMEM event type (0 = observation, 1 = dose)
DV	Observation (DVA = viable parasites, PRR = log10(viable+1))
FLAG	1 = DVA, 2 = PRR
MDV	1 = ignored by NONMEM, 0 = used
REPLICATE	Experimental replicate
CA, CB, CC, CD	Concentrations of compounds (DHA, CQ, ATO, PYRI)

📊 Dataset Structure by ID

Sample Type	ID	Data Type	Drug
3D7	ID = 1	DVA	DHA
3D7	ID = 2	DVA	CQ
3D7	ID = 3	DVA	ATO
3D7	ID = 4	DVA	PYRI
3D7	ID = 5	PRR	DHA
3D7	ID = 6	PRR	CQ
3D7	ID = 7	PRR	ATO
3D7	ID = 8	PRR	PYRI
Field Isolates	ID = 9	DVA	DHA
Field Isolates	ID = 10	DVA	CQ
Field Isolates	ID = 11	DVA	ATO
Field Isolates	ID = 12	DVA	PYRI

⚠️ For each run, make sure to modify the ACCEPT=(ID.EQ.X) clause in the $DATA block to target the desired experiment.

🔄 What to Update for Each Run

1. Select the appropriate ID depending on the experiment (ACCEPT=(ID.EQ.X))
2. Update the model file name (e.g., run001.mod)
3. Update output filenames in $TABLE, if needed : sdtab00X and patab00X

📦 Output Files

• sdtab00X: Individual data + predictions (IPRED)
• patab00X: Estimated parameter values

✍️ Author

NONMEM model written by Mohamed MAIGA and Sebastian Wicha Year: 2025