# Creating Figures in Partition Coefficient Evaluation

#### Robert Pearce

#### 2017-06-08

This vignette generates the data and figures from the partition coefficient evaluation paper.

    library(httk)
    library(gdata)
    library(ggplot2)
    library(viridis)
    library(CensRegMod)
    library(gmodels)
    library(gplots)
    library(scales)
    library(colorspace)

We first filter the measured rat Kp data, pc.data. Then the old and new Kp predictions are made, along with error and improvement measures, and these are all consolidated into a table for analysis and plotting. Note that the final table contains log10-transformed values and error and improvements derived from subtracting these values. Only relevant rat values are used. Compounds with Funbound.plasma and partition coefficients of zero are removed as well as compounds with approximated Funbound.plasma values.

    pc.table <- NULL
    pc.data <- subset(pc.data,fu != 0 & Exp_PC != 0 & Tissue %in% c("Adipose","Bone","Brain","Gut",
        "Heart","Kidney","Liver","Lung","Muscle","Skin","Spleen","Blood Cells") &
        tolower(Species) == 'rat' & !CAS %in% c('10457-90-6','5786-21-0','17617-23-1','69-23-8','2898-12-6',
        '57562-99-9','59-99-4','2955-38-6','155-97-5','41903-57-5','58-55-9','77-32-7','59-05-2','60-54-8'))
    cas.list <- get_cheminfo(model='schmitt',species='rat')
    cas.list <-  cas.list[cas.list %in% pc.data[,'CAS']]
    ma.data.list <- subset(chem.physical_and_invitro.data,!is.na(logMA))[,'CAS']
    for(this.cas in cas.list){
      parameters <- parameterize_schmitt(chem.cas=this.cas,species='rat')
      init.parameters <- parameters
      charge <- calc_ionization(chem.cas=this.cas,pH=7.4)$fraction_charged
      if(!this.cas %in% ma.data.list){
        init.parameters$MA <- 10^(0.999831 - 0.016578*38.7 + 0.881721 * log10(parameters$Pow))
      }
      pcs <- predict_partitioning_schmitt(parameters=parameters,species='rat',regression=F)
      init.pcs <- predict_partitioning_schmitt(parameters=init.parameters,species='rat',regression=F)
      for(this.tissue in subset(pc.data,CAS==this.cas)[,'Tissue']){
        if(this.tissue == 'Blood Cells') this.pc <- 'rbc'
        else this.pc <- this.tissue
        pc.table <- rbind(pc.table,cbind(as.data.frame(this.cas),as.data.frame(this.tissue),
        as.data.frame(log10(init.pcs[[which(substr(names(init.pcs),2,nchar(names(init.pcs))-3)
                                      == tolower(this.pc))]] * init.parameters$Funbound.plasma)),
        as.data.frame(log10(pcs[[which(substr(names(pcs),2,nchar(names(pcs))-3)
                                 == tolower(this.pc))]] * parameters$Funbound.plasma.uncorrected)),
        as.data.frame(log10(init.pcs[[which(substr(names(init.pcs),2,nchar(names(init.pcs))-3)
                                      == tolower(this.pc))]] * init.parameters$Funbound.plasma.uncorrected)),
        as.data.frame(log10(pcs[[which(substr(names(pcs),2,nchar(names(pcs))-3)
                                 == tolower(this.pc))]] * parameters$Funbound.plasma)),
        as.data.frame(log10(subset(pc.data,CAS==this.cas & Tissue==this.tissue)[,'Exp_PC'])),
        as.data.frame(subset(pc.data,CAS==this.cas & Tissue==this.tissue)[,'LogP']),as.data.frame(charge),
        as.data.frame(as.character(subset(pc.data,CAS == this.cas)[1,'A.B.N'])),
        as.data.frame(subset(pc.data,CAS == this.cas)[1,'fu'])))
      }
    }
    colnames(pc.table) <- c('CAS','Tissue','fup.correction','ma.correction','init.Predicted',
                            'Predicted','Experimental','logP','charge','type','fup')
    init.error <- pc.table[,'Experimental'] - pc.table[,'init.Predicted']
    fup.error <- pc.table[,'Experimental'] - pc.table[,'fup.correction']
    ma.error <- pc.table[,'Experimental'] - pc.table[,'ma.correction']
    final.error <- pc.table[,'Experimental'] - pc.table[,'Predicted']
    fup.improvement <- abs(init.error) - abs(fup.error)
    ma.improvement <- abs(init.error) - abs(ma.error)
    final.improvement <- abs(init.error) - abs(final.error)
    pc.table <- cbind(pc.table,fup.improvement,ma.improvement, final.improvement,
                      final.error,init.error,ma.error,fup.error)

    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))
    }

    init.plot <- ggplot() + geom_point(data=pc.table,aes(10^(init.Predicted),10^(Experimental))) +
        geom_abline() + labs(y=expression(paste("Measured ",K[p])), x=expression(paste("Predicted ",K[p]))) +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16),
        plot.title=element_text(size=18,hjust = 0.5)) + scale_x_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_y_log10(label=scientific_10,limits=c(0.01,10^4.5)) + ggtitle('(A)')

    final.plot <- ggplot() + geom_point(data=pc.table,aes(10^(Predicted),10^(Experimental))) +
        geom_abline() + labs(y=expression(paste("Measured ",K[p])),x=expression(paste("Predicted ",K[p]))) +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16),
        plot.title=element_text(size=18,hjust=0.5)) + scale_x_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_y_log10(label=scientific_10,limits=c(0.01,10^4.5)) + ggtitle('(B)')

    fup.change.plot <-  ggplot() +
        geom_point(data=pc.table[order(pc.table[,'fup.improvement'],decreasing=F),],
        aes(10^(fup.correction),10^(Experimental),color=fup.improvement)) + geom_abline() +
        labs(y=expression(paste("Measured ",K[p])),x=expression(paste("Predicted ",K[p])),color='Improvement') +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16)) +
        scale_x_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_y_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_color_viridis(direction=-1,option='inferno')
    ma.subset <- subset(pc.table,!CAS %in% ma.data.list)
    ma.change.plot <- ggplot() +
        geom_point(data=ma.subset[order(ma.subset[,'ma.improvement'],decreasing=F),]
        ,aes(10^(ma.correction),10^(Experimental),color=ma.improvement)) + geom_abline() +
        labs(y=expression(paste("Measured ",K[p])),x=expression(paste("Predicted ",K[p])),color='Improvement') +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16)) +
        scale_x_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_y_log10(label=scientific_10,limits=c(0.01,10^4.5)) +
        scale_color_viridis(direction=-1,option='inferno')

Now we calculate and plot the regressions for all tissues, together with their 95% confidence intervals.

    regressions <- NULL

    for(tissue in as.character(unique(pc.table[,'Tissue']))){
      fit <- lm(Experimental ~ Predicted ,data=subset(pc.table,Tissue==tissue))
      smry <- summary(fit)
      est <- estimable(fit, cm=diag(2), beta0=c(0,1), joint.test=TRUE)
      regressions <- rbind(regressions,cbind(tissue,as.data.frame(fit$coefficients[['(Intercept)']]),
          as.data.frame(fit$coefficients[['Predicted']]),
          as.data.frame(smry$coefficients[['Predicted','Pr(>|t|)']]),
          as.data.frame(smry$sigma),as.data.frame(smry$r.squared),
          as.data.frame(smry[[11]][1,1]),as.data.frame(smry[[11]][2,2]),
          as.data.frame(smry[[11]][1,2]),as.data.frame(smry$df[2]),as.data.frame(est[[3]])))
    }
    colnames(regressions) <- c('Tissue','Intercept','Slope','P-value','SE','R-squared',
                               'Int Var','Slp Var','Cov','df','estimable')
    x.cf <- seq(-2,3.5,.01)
    for(tissue in  as.character(unique(pc.table[,'Tissue']))){
      conf <- qt(0.975,df=subset(regressions,Tissue==tissue)[['df']]+1) *
          subset(regressions,Tissue==tissue)[['SE']] *
          sqrt(subset(regressions,Tissue==tissue)[['Int Var']] +
          x.cf^2 * subset(regressions,Tissue==tissue)[['Slp Var']] +
          2 * x.cf * subset(regressions,Tissue==tissue)[['Cov']] + 1)
      line <- subset(regressions,Tissue==tissue)[['Intercept']] +
          x.cf * subset(regressions,Tissue==tissue)[['Slope']]
      y.cf <- line + conf
      y.ncf <- line - conf

      cf <- cbind(as.data.frame(x.cf),as.data.frame(y.cf),as.data.frame(y.ncf))
      if(tissue == 'Blood Cells'){
        eval(parse(text= paste('Blood <- ggplot() + labs(y=expression(paste("Inferred ",K[p])),
                                   x=expression(paste("Predicted ",K[p]))) + geom_abline(linetype = "dashed") +
                                   geom_point(data=subset(pc.table,Tissue == \'',tissue,'\'),aes(10^(Predicted),
                                   10^(Experimental)))  +  theme(axis.text=element_text(size=14),
                                   axis.title=element_text(size=14),plot.title=element_text(size=14)) +
                                   scale_x_log10(label=scientific_10,limits=c(0.01,1000)) +
                                   scale_y_log10(label=scientific_10,limits=c(0.01,1000)) +
                                   geom_line(data=cf,aes(10^(x.cf),10^(y.cf))) +
                                   geom_line(data=cf,aes(10^(x.cf),10^(y.ncf))) +
                                   geom_abline(intercept=subset(regressions,Tissue==tissue)[[\'Intercept\']],
                                   slope=subset(regressions,Tissue==tissue)[[\'Slope\']]) +
                                   ggtitle(\'Red Blood Cells\')',sep='')))
      }else{
        eval(parse(text= paste(tissue,' <- ggplot() + labs(y=expression(paste("Measured ",K[p]))
                                   ,x=expression(paste("Predicted ",K[p]))) + geom_abline(linetype = "dashed") +
                                   geom_point(data=subset(pc.table,Tissue == \'',tissue,'\'),
                                   aes(10^(Predicted),10^(Experimental))) + theme(axis.text=element_text(size=14),
                                   axis.title=element_text(size=14),plot.title=element_text(size=14)) +
                                   scale_x_log10(label=scientific_10,limits=c(0.01,1000)) +
                                   scale_y_log10(label=scientific_10,limits=c(0.01,1000)) +
                                   geom_line(data=cf,aes(10^(x.cf),10^(y.cf))) +
                                   geom_line(data=cf,aes(10^(x.cf),10^(y.ncf))) +
                                   geom_abline(intercept=subset(regressions,Tissue==tissue)[[\'Intercept\']],
                                   slope=subset(regressions,Tissue==tissue)[[\'Slope\']]) +
                                   ggtitle(\'',tissue,'\')',sep='')))
      }
    }

In vivo volume of distribution data are compared with the predictions, and errors are calculated. Regressions and improvements are calculated and plotted.

    obach <- subset(Obach2008,CAS %in% get_cheminfo(model='schmitt'))
    vd.table <- NULL

    for(this.cas in obach[,'CAS']){
      parameters <- parameterize_schmitt(chem.cas=this.cas)
      init.parameters <- parameters
      if(!this.cas %in% ma.data.list){
        init.parameters$MA <- 10^(0.999831 - 0.016578*37 + 0.881721 * log10(parameters$Pow))
      }
      pcs <- predict_partitioning_schmitt(parameters=parameters,regression=F)
      init.pcs <- predict_partitioning_schmitt(parameters=init.parameters,regression=F)
      reg.pcs <- predict_partitioning_schmitt(parameters=parameters,regression=T)
      vdist <- calc_vdist(parameters=c(pcs,Funbound.plasma=parameters$Funbound.plasma))
      init.vdist <- calc_vdist(parameters=c(init.pcs,Funbound.plasma=parameters$Funbound.plasma.uncorrected))
      reg.vdist <- calc_vdist(parameters=c(reg.pcs,Funbound.plasma=parameters$Funbound.plasma))
      vd.table <- rbind(vd.table,cbind(as.data.frame(this.cas),as.data.frame(log10(init.vdist)),
                        as.data.frame(log10(vdist)),as.data.frame(log10(reg.vdist)),
                        as.data.frame(log10(subset(obach,CAS==this.cas)[['VDss (L/kg)']]))))
    }
    colnames(vd.table) <- c('CAS','init.vdist','corrected.vdist','calibrated.vdist','Experimental')
    init.error <- vd.table[,'Experimental'] - vd.table[,'init.vdist']
    correction.error <- vd.table[,'Experimental'] - vd.table[,'corrected.vdist']
    calibration.error <- vd.table[,'Experimental'] - vd.table[,'calibrated.vdist']
    correction.improvement <- abs(init.error) - abs(correction.error)
    calibration.improvement <- abs(correction.error) - abs(calibration.error)
    vd.table <- cbind(vd.table,correction.improvement,calibration.improvement,
                      init.error,correction.error,calibration.error)
    fit <- lm(Experimental ~ calibrated.vdist,data=vd.table)
    smry <- summary(fit)
    calibrated.reg <- cbind(as.data.frame(fit$coefficients['(Intercept)']),
                            as.data.frame(fit$coefficients['calibrated.vdist']),
                            as.data.frame(smry$coefficients['calibrated.vdist','Pr(>|t|)']),
                            as.data.frame(smry$sigma),as.data.frame(smry$r.squared))
    fit <- lm(Experimental ~ init.vdist,data=vd.table)
    smry <- summary(fit)
    init.reg <- cbind(as.data.frame(fit$coefficients['(Intercept)']),
                      as.data.frame(fit$coefficients['init.vdist']),
                      as.data.frame(smry$coefficients['init.vdist','Pr(>|t|)']),
                      as.data.frame(smry$sigma),as.data.frame(smry$r.squared))
    fit <- lm(Experimental ~ corrected.vdist,data=vd.table)
    smry <- summary(fit)
    corrected.reg <- cbind(as.data.frame(fit$coefficients['(Intercept)']),
                           as.data.frame(fit$coefficients['corrected.vdist']),
                           as.data.frame(smry$coefficients['corrected.vdist','Pr(>|t|)']),
                           as.data.frame(smry$sigma),as.data.frame(smry$r.squared))
    colnames(init.reg) <- colnames(corrected.reg) <-
    colnames(calibrated.reg) <- c('Intercept','Slope','P-value','Std Err','R-squared')

    init.vd.plot <- ggplot(vd.table,aes(10^(init.vdist),10^(Experimental))) + geom_point() +
        geom_abline(intercept = init.reg[['Intercept']], slope = init.reg[["Slope"]]) +
        geom_abline(linetype = "dashed") + xlab("Predicted Volume of Distribution") +
        ylab("Measured Volume of Distribution") + theme(axis.text=element_text(size=16),
        axis.title=element_text(size=16),plot.title=element_text(size=18,hjust = 0.5)) +
        scale_x_log10(label=scientific_10,limits=c(10^(-1.5),10^(8.5))) +
        scale_y_log10(label=scientific_10,limits=c(10^(-1.5),10^(8.5))) +
        ggtitle('(A)')

    correction.plot <- ggplot() +
        geom_point(data=vd.table[order(vd.table[,'correction.improvement'],decreasing=F),],
        aes(10^(corrected.vdist),10^(Experimental),color=correction.improvement)) +
        geom_abline(intercept = corrected.reg[['Intercept']],slope = corrected.reg[["Slope"]]) +
        geom_abline(linetype = "dashed") + xlab("Predicted Volume of Distribution") +
        ylab("Measured Volume of Distribution") + theme(legend.position = c(.95, .95),
        legend.justification = c("right", "top"),legend.box.just = "right",
        legend.margin = margin(6, 6, 6, 6),axis.text=element_text(size=16),
        axis.title=element_text(size=16),plot.title=element_text(size=18,hjust = 0.5)) +
        scale_x_log10(limits=c(10^(-1.5),10^(3))) + scale_y_log10(limits=c(10^(-1.5),10^(3))) +
        ggtitle('(B)') + scale_color_viridis(direction=-1,option='inferno')

    calibration.plot <- ggplot() +
        geom_point(data=vd.table[order(vd.table[,'calibration.improvement'],decreasing=F),],
        aes(10^(calibrated.vdist),10^(Experimental),color=calibration.improvement)) +
        geom_abline(intercept = calibrated.reg[['Intercept']],slope = calibrated.reg[["Slope"]]) +
        geom_abline(linetype = "dashed") + xlab("Predicted Volume of Distribution") +
        ylab("Measured Volume of Distribution") + theme(legend.position = c(.95, .95),
        legend.justification = c("right", "top"),legend.box.just = "right",
        legend.margin = margin(6, 6, 6, 6),axis.text=element_text(size=16),
        axis.title=element_text(size=16),plot.title=element_text(size=18,hjust = 0.5)) +
        scale_x_log10(limits=c(10^(-1.5),10^(3))) + scale_y_log10(limits=c(10^(-1.5),10^(3))) +
        ggtitle('(C)') + scale_color_viridis(direction=-1,option='inferno')

Now we pull in the in vivo blood to plasma ratios, use these to calculate the inferred red blood cell to plasma ratios, and then make predictions for these values. A censored regressions is performed, and predictions are plotted against errors.

    rb2p.data <- subset(chem.physical_and_invitro.data,!is.na(Human.Rblood2plasma))
    measured.rb2p <- NULL
    measured.krbc <- NULL
    predicted.rb2p <- NULL
    predicted.krbc <- NULL
    cas <- NULL
    charge <- NULL
    fup <- NULL
    logP <- NULL
    pka_donor <- NULL
    pka_accept <- NULL
    for(this.cas in rb2p.data[rb2p.data[,'CAS'] %in% get_cheminfo(model='schmitt'),'CAS']){
      rb2p <- get_rblood2plasma(chem.cas=this.cas)
      krbc <- (rb2p + .44 - 1) / 0.44
      measured.rb2p <- c(measured.rb2p,rb2p)
      measured.krbc <- c(measured.krbc,krbc)
      parameters <- parameterize_schmitt(chem.cas=this.cas)
      pcs <- predict_partitioning_schmitt(parameters=parameters)
      predicted.krbc <- c(predicted.krbc,pcs[['Krbc2pu']] * parameters$Funbound.plasma)
      cas <- c(cas,this.cas)
      charge <- c(charge,calc_ionization(chem.cas=this.cas,pH=7.4)$fraction_charged)
      fup <- c(fup,parameters$Funbound.plasma.uncorrected)
      logP <-  c(logP,log10(parameters$Pow))
      pka_donor <- c(pka_donor,paste(parameters$pKa_Donor,collapse=','))
      pka_accept <- c(pka_accept,paste(parameters$pKa_Accept,collapse=','))
    }
    predicted.rb2p <-  1 - 0.44 + 0.44 * predicted.krbc
    rb2p.table <- cbind(as.data.frame(cas),as.data.frame(predicted.rb2p),as.data.frame(measured.rb2p))
    colnames(rb2p.table) <- c('cas','predicted.rb2p','measured.rb2p')
    error <- log10(rb2p.table[,'measured.rb2p']) - log10(rb2p.table[,'predicted.rb2p'])
    rb2p.table <- cbind(rb2p.table,error,charge,fup,logP)

    error <- log10(measured.krbc) -  log10(predicted.krbc)
    krbc.table <- cbind(as.data.frame(cas),as.data.frame(predicted.krbc),as.data.frame(measured.krbc),
                        as.data.frame(error),charge,fup,logP,pka_donor,pka_accept)

    pdta <- data.frame(x = predicted.krbc,
                       y = measured.krbc)
    pdta$y[pdta$y <= 0.1] <- 0.1
    pdta$Censoring <- factor(c("Not Censored","Censored")[as.numeric(pdta$y <= 0.1) + 1])
    y <- measured.krbc
    x <- cbind(rep(1, length(y)),-1 * log10(predicted.krbc))
    colnames(x) <- c("Intercept","Predicted")
    cc <- as.numeric(y <= 0.1)
    y[y < 0.1] <- 0.1
    y <- -log10(y)

    out <- em.cens(cc, x, y, dist="Normal")

    censored.regression <- ggplot() +
        geom_point(data=pdta,aes(x=x,y=y, color=Censoring)) +
        scale_x_log10(limits=c(.0009,40)) + scale_y_log10(limits=c(.1,4),breaks=c(.1,.5,2.5)) +
        labs(y=expression(paste("Inferred ",K[p])),x=expression(paste("Predicted ",K[p]))) +
        geom_abline(intercept=0, slope=1, linetype='dashed') +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16),
        plot.title=element_text(size=18,hjust=0.5),legend.position = c(0.11, .8)) +
        geom_abline(slope=out$betas[2],intercept=-out$betas[1]) + ggtitle('(B)')

    rb2p.plot <- ggplot(rb2p.table,aes(predicted.rb2p,measured.rb2p)) +
        geom_point()  + scale_x_log10(lim=c(.52,18)) +
        scale_y_log10(lim=c(.52,2.5),breaks=c(0.5,1,2)) + geom_abline(linetype='dashed') +
        labs(y=expression(paste("Measured Whole Blood ",K[p])),
        x=expression(paste("Predicted Whole Blood ",K[p]))) +
        theme(axis.text=element_text(size=16),axis.title=element_text(size=16),
        plot.title=element_text(size=18,hjust=0.5)) + ggtitle('(A)')

Lastly, we make the heatmap.

    heatmap.table <- NULL
    for(this.cas in get_cheminfo(model='schmitt')){
        parms <- parameterize_schmitt(chem.cas=this.cas)
        pcs <- predict_partitioning_schmitt(parameters=parms)
        heatmap.table <- cbind(heatmap.table,log10(unlist(pcs)[1:11]*parms$Funbound.plasma))
    }
    rownames(heatmap.table) <-  c('Adipose','Bone','Brain','Gut','Heart',
                                  'Kidney','Liver','Lung','Muscle','Skin','Spleen')
    colnames(heatmap.table) <- rep("",dim(heatmap.table)[2])

    pal <- function (n, h = c(260, -328), c = 80, l = c(30, 100), power = 1.5,
        fixup = TRUE, gamma = NULL, alpha = 1, ...)
    {
        if (!is.null(gamma))
            warning("'gamma' is deprecated and has no effect")
        if (n < 1L)
            return(character(0L))
       h <- rep(h, length.out = 2L)
        c <- c[1L]
        l <- rep(l, length.out = 2L)
        power <- rep(power, length.out = 2L)
        rval <- seq(1, -1, length = n)
        rval <- hex(polarLUV(L = l[2L] - diff(l) * abs(rval)^power[2L],
            C = c * abs(rval)^power[1L], H = ifelse(rval > 0, h[1L],
                h[2L])), fixup = fixup, ...)
        if (!missing(alpha)) {
            alpha <- pmax(pmin(alpha, 1), 0)
            alpha <- format(as.hexmode(round(alpha * 255 + 1e-04)),
                width = 2L, upper.case = TRUE)
            rval <- paste(rval, alpha, sep = "")
        }
        return(rval)
    }

    hclust.ave <- function(x) hclust(x, method="ward.D2")
    heatmap.2(heatmap.table,col=pal,trace="none", hclustfun=hclust.ave,
              key.xlab=expression(paste("log10 ",K[p]," Value")),
              key.ylab=expression(paste("Number of ",K[p])),
              key.title="Partition Coefficient",xlab="Chemicals",cex.lab=2,margins=c(2,5))