Drug Repurposing

This project aims to predict clinically relevant in vitro drug concentrations from pharmacokinetic parameters and drug-protein interaction assays.

The file structure is as follows,

.
├── Figures            # code to generate .ai figure files from raw data
│   ├── Figure1        # meta-analysis of clinical trial success across TKIs and preclinical analysis of imatinib and erlotinib
│   ├── Figure2        # heatmaps of various metrics of in vitro dose response curves (IC50-fold change, free alpha, effective alpha, etc) and associated ROC curves
│   ├── Figure3        # mapping of closed-form solution of analytical drug binding model to parameter space and analysis of protein titration experiments
│   ├── Figure4        # logistic models and their evaluation, including the final effective C_ave model (LogisticRegressionModels.rds)
│   ├── Figure5        # results of logistic effective C_ave model for EGFR and KIT inhibitors
│   ├── Figure6        # synthesis of clinical and preclinical meta-analysis for imatinib
├── ReferenceFiles     # where raw data is stored
├── Supplement         # code and resources to generate manuscript supplement
│   ├── SuppAppendices # code to map solutions of analytical off-targeting drug binding model
│   ├── SuppFigures    # collection of dendrogram, GR50, serum protein titration, and free C_ave analyses
│   ├── SuppMethods    # description of clinical and preclinical meta-analysis methods
│   ├── SuppTables     # collection of supplemental tables used in analysis
├── WebApp             # raw code for R shiny app for custom serum-shift experiments

Installation

After cloning the repository, install the following required R packages

# Package names
packages = c("rstudioapi", "ggplot2", "RColorBrewer", "scales", "reshape2", "pROC", "viridis", "dr4pl")

# Install packages not yet installed
installed_packages = packages %in% rownames(installed.packages())
if (any(installed_packages == FALSE)) {
  install.packages(packages[!installed_packages])
}

Data

The repo here contains all data and code necessary to generate the logistic classifiers and analyze the results. Raw data can be found in the ReferenceFiles folder.

Manuscript

The code used to generate figures in the manuscript are organized in the Figures folder.