mPBPK/mTPA project

We provide a framework for model-based assessment of target engagement response of antibody candidates by integrating a mPBPK with a tree-based machine learning model. We generate a large number of synthetic drug-target candidate pairs with varying properties using the mPBPK/TMDD model. These synthetic drug and target properties are categorized into optimal or non-optimal spaces according to a desired target occupancy percentage. An interpretable decision-tree based algorithm was applied for high-throughput screening of synthetic drug-target properties such as binding affinity, baseline concentration, target half-life, dose, dosing scheme, and antibody charge to identify the combination of compound properties that are most likely to demonstrate the desired target engagement response. (https://link.springer.com/article/10.1007/s10928-023-09899-z)

Jupyter notebooks

1. mTPA_local (Ts).ipynb : Binary Decision tree-based ML model based on PK data for soluble receptors.
   
2. mTPA_local (Tm).ipynb : Binary Decision tree-based ML model based on PK data for membrane-bound receptors.
   
3. mTPA_local (3-label).ipynb : Three-label Decision tree-based ML classifier based on PK data.
   

Dependencies

Package dependencies in Python (version 3.8.3):
Pandas, sklearn, numpy, matplotlib, joblib, pickle, imblearn, scipy

Instructions for Use

1. Input
   Example files to run Jupyter notebooks:
   

a. In Silico candidates
~ Call "target_candLOG10000.csv" example file as input to ML model.
b. In Silico PK data
~ Call an PK endpoint (TO%) example file. Each file is named as follows: PK_endpoint_(dose)(scheme)(species)_(receptor)10000.csv
~ From PK endpoints, use column "TOplast" as the ML criterion. TOplast: TO% calculated at Cmin in plasma.

Note: To use your own data to run these codes. The ML input data format must same as example input files. Criterion for ML output can be TO% calculated at any PK endpoint (Cmin, Cmax etc.).

2. Output
   a. ML model output includes ~ Trained decision Tree Model
   ~ Decision Tree Plot
   ~ Confusion Matrix
   ~ Classification Metric
   ~ Scatter and Kernel Density plots
   ~ ROC curve
   ~ Predicted (.csv) file with optimal and non-optimal candidates
   

Versions

These source codes were written in Python 3 version.