---
title: "Frank et al. (2018): Creating IVIVE Figure (Fig. 6)"
author: "John F. Wambaugh"
date: "September 25, 2019"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Frank et al. (2018): Creating IVIVE Figure (Fig. 6)}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

from "Defining toxicological tipping points in neuronal network development"

Christopher L. Frank, Jasmine P. Brown, Kathleen Wallace, John F. Wambaugh, Imran Shah, and Timothy J. Shafer

Toxicology and Applied Pharmacology 354 (2018) 81-93

https://doi.org/10.1016/j.taap.2018.01.017

## Abstract
Measuring electrical activity of neural networks by microelectrode array (MEA) has recently 
shown promise for screening level assessments of chemical toxicity on network development 
and function. Important aspects of interneuronal communication can be quantified from a 
single MEA recording, including individual firing rates, coordinated bursting, and measures 
of network synchrony, providing rich datasets to evaluate chemical effects. Further, 
multiple recordings can be made from the same network, including during the formation of 
these networks in vitro. The ability to perform multiple recording sessions over the in 
vitro development of network activity may provide further insight into developmental 
effects of neurotoxicants. In the current study, a recently described MEA-based screen of 
86 compounds in primary rat cortical cultures over 12 days in vitro was revisited to 
establish a framework that integrates all available primary measures of electrical 
activity from MEA recordings into a composite metric for deviation from normal activity 
(total scalar perturbation). Examining scalar perturbations over time and increasing 
concentration of compound allowed for definition of critical concentrations or "tipping 
points" at which the neural networks switched from recovery to non-recovery trajectories
for 42 compounds. These tipping point concentrations occurred at predominantly lower 
concentrations than those causing overt cell viability loss or disrupting individual 
network parameters, suggesting tipping points may be a more sensitive measure of network 
functional loss. Comparing tipping points for six compounds with plasma concentrations 
known to cause developmental neurotoxicity in vivo demonstrated strong concordance and
suggests there is potential for using tipping points for chemical prioritization.


## Prepare for session
### Load the relevant libraries
```{r setup, eval = FALSE}
knitr::opts_chunk$set(echo = TRUE, fig.width=5, fig.height=4)
library(httk)
#library(gdata)
library(ggplot2)
library(scales)
```
### Function to format scientific notation 
From https://stackoverflow.com/questions/10762287/how-can-i-format-axis-labels-with-exponents-with-ggplot2-and-scales
```{R scientific.notation}
scientific_10 <- function(x) {                                  
  out <- gsub("1e", "10^", scientific_format()(x))              
  out <- gsub("\\+","",out)                                     
  out <- gsub("10\\^01","10",out)                               
  out <- parse(text=gsub("10\\^00","1",out))                    
}  
```
### Load the data
```{r Frank2018data, eval = FALSE}
chem.table <- Frank2018invivo
```
## Use HTTK to perform IVIVE
### This loops through each study design in the table and runs solve_pbtk:
```{R ivive.loop, eval = FALSE}
for (this.row in 1:dim(chem.table)[1])
{
  this.cas <- chem.table[this.row,"Substance_CASRN"]
  if (tolower(chem.table[this.row,"Species"])=="rodent") 
  {  
    this.species <- "rat"
  } else if (tolower(chem.table[this.row,"Species"])=="rat") 
  {
    this.species <- "rat"
  } else if (tolower(chem.table[this.row,"Species"])=="human")
  {
    this.species <- "human"
  }
  else if (tolower(chem.table[this.row,"Species"])=="mouse") 
  {
    this.species <- "mouse"
  }
  else browser()
  if (chem.table[this.row,"Route"] %in% c("i.p.","s.c.","i.m.")) iv.dose =TRUE
  else if (chem.table[this.row,"Route"]=="oral") iv.dose = F
  else browser()
  this.dose <- chem.table[this.row,"Dose"]
  this.days <- chem.table[this.row,"Days"]
# Make sure the dose units are in mg/kg body weight:
  if (regexpr("ug",chem.table[this.row,"Dose.Units"])!=-1) 
  {
    this.dose <- this.dose/1000
  }
  if (regexpr("/kg",chem.table[this.row,"Dose.Units"])==-1) 
  {
    this.dose <- this.dose/0.25
  }
# Here we run the HTTK PBPK Model:
  out <- suppressWarnings(solve_pbtk(chem.cas=this.cas,
           dose=this.dose,
           species=this.species,
# This was used in 2017 but I don't agree with it anymore:
#           restrictive.clearance=FALSE,
           days=this.days,
           iv.dose=iv.dose,
           default.to.human=TRUE))

# Record the Cmax and the AUC:
  chem.table[this.row,"Cmax"] <- max(out[,"Cplasma"])
  chem.table[this.row,"AUC"] <- max(out[,"AUC"])
}
```
## Make the plot
Comparison between predicted plasma levels for critical
concentrations and in vivo estimates from the httk model. For
those chemicals with 1) in vitro predicted critical concentrations,
2) in vivo studies indicating neurological effect, and 3)
available toxicokinetic data the time-integrated plasma concentration
(area under the curve or AUC) was predicted for the
LOEL associated with each chemical-specific study. The chemical-
specific prediction is indicated by the first four letters of
each chemicals name. There were two available studies for each
chemical. The identity ("perfect predictor") line is indicated by
a solid black line, while the dashed lines indicate ten-fold above
and below perfect prediction. Because all in vitro treatments
were exposed for the same amount of time, the relationship
between nominal in vitro concentration and time-integrated
concentration is a constant.
```{R Frank2018.Fig6, eval = FALSE}
Fig.AUC <- ggplot(data=chem.table) +
  geom_segment(color="grey",aes(x=AUC,y=Lower.95..CI,xend=AUC,yend=Higher.95..CI))+    
#  geom_point(aes(x=AUC,y=Critical.concentration,color="Chemical"))+ 
   geom_text(aes(x=AUC,y=Critical.concentration,label=Compound.abbrev,color=Chemical)) +
   scale_y_log10(label=scientific_10,limits=c(10^-7,100)) +
   scale_x_log10(label=scientific_10,limits=c(10^-7,100)) +
    annotation_logticks() + 
    geom_abline(slope=1, intercept=0) + 
    geom_abline(slope=1, intercept=1,linetype="dashed") + 
    geom_abline(slope=1, intercept=-1,linetype="dashed") + 
    xlab(expression(paste(italic("In vivo")," AUC estimated with HTTK (uM*day)"))) + 
    ylab(expression(paste(italic("In vitro")," predicted Critical Conc. (uM)"))) +
    scale_color_brewer(palette="Set2") + 
    theme_bw()  +
    theme(legend.position="bottom")
    
print(Fig.AUC)
```
