---
title: "Frank et al. (2018): Neuronal Network IVIVE"
author: "John F. Wambaugh"
date: "September 25, 2019"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{c) Frank (2018): Neuronal Network IVIVE}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---
*Please send questions to wambaugh.john@epa.gov*

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.

## HTTK Version

This vignette was created with **httk** v1.8. Although we attempt to maintain 
backward compatibility, if you encounter issues with the latest release of 
**httk**
and cannot easily address the changes, historical versions of httk are 
available from: https://cran.r-project.org/src/contrib/Archive/httk/

## Prepare for session
R package **knitr** generates html and PDF documents from this RMarkdown file,
Each bit of code that follows is known as a "chunk". We start by telling 
**knitr** how we want our chunks to look.
```{r knitrPrep, include=FALSE, eval = TRUE}
knitr::opts_chunk$set(echo = TRUE, fig.width=5, fig.height=4)
```

### Clear the memory
It is a bad idea to let variables and other information from previous R
sessions float around, so we first remove everything in the R memory.
```{r clear_memory, eval = TRUE}
rm(list=ls()) 
```

### eval = execute.vignette
If you are using the RMarkdown version of this vignette (extension, .RMD) you
will be able to see that several chunks of code in this vignette
have the statement "eval = execute.vignette". The next chunk of code, by default,
sets execute.vignette = FALSE.  This means that the code is included (and necessary) but was
not run when the vignette was built. We do this because some steps require 
extensive computing time and the checks on CRAN limit how long we can spend
building the package. If you want this vignette to work, you must run all code,
either by cutting and pasting it into R. Or, if viewing the .RMD file, you can
either change execute.vignette to TRUE or press "play" (the green arrow) on
each chunk in RStudio.
```{r runchunks, eval = TRUE}
# Set whether or not the following chunks will be executed (run):
execute.vignette <- FALSE
```

### Load the relevant libraries
We use the command 'library()' to load various R packages for our analysis.
If you get the message "Error in library(X) : there is no package called 'X'"
then you will need to install that package: 

From the R command prompt:

install.packages("X")

Or, if using RStudio, look for 'Install Packages' under 'Tools' tab.

```{r setup, eval = execute.vignette}
library(httk)
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, eval = execute.vignette}
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 = execute.vignette}
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 = execute.vignette}
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 = execute.vignette}
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)
```
