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<h1 class="title toc-ignore">PK - Multiple Ascending Dose</h1>
<h4 class="author">Alison Margolskee</h4>
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<div id="overview" class="section level2">
<h2>Overview</h2>
<!--START_EXPLANATION-->
<p>This document contains exploratory plots for multiple ascending dose
PK data as well as the R code that generates these graphs. The plots
presented here are based on simulated data (<a
href="PKPD_Datasets.html">see: PKPD Datasets</a>). Data specifications
can be accessed on <a href="Datasets.html">Datasets</a> and Rmarkdown
template to generate this page can be found on <a
href="Rmarkdown/Multiple_Ascending_Dose_PK.Rmd">Rmarkdown-Template</a>.
You may also download the Multiple Ascending Dose PK/PD dataset for your
reference (<a href="Data/Multiple_Ascending_Dose_Dataset2.csv">download
dataset</a>). <!--END_EXPLANATION--></p>
</div>
<div id="setup" class="section level2">
<h2>Setup</h2>
<pre class="r"><code>library(ggplot2)
library(dplyr)
library(tidyr)
library(xgxr)
#flag for labeling figures as draft
status = "DRAFT"
## ggplot settings
xgx_theme_set()</code></pre>
</div>
<div id="load-dataset" class="section level2">
<h2>Load Dataset</h2>
<pre class="r"><code>#load dataset
pkpd_data <- read.csv("../Data/Multiple_Ascending_Dose_Dataset2.csv")
DOSE_CMT = 1
PK_CMT = 2
SS_PROFDAY = 6 # steady state prof day
TAU = 24 # time between doses, units should match units of TIME, e.g. 24 for QD, 12 for BID, 7*24 for Q1W (when units of TIME are h)
#ensure dataset has all the necessary columns
pkpd_data = pkpd_data %>%
mutate(ID = ID, #ID column
TIME = TIME, #TIME column name
NOMTIME = NOMTIME, #NOMINAL TIME column name
PROFDAY = case_when(
NOMTIME < (SS_PROFDAY - 1)*24 ~ 1 + floor(NOMTIME / 24),
NOMTIME >= (SS_PROFDAY - 1)*24 ~ SS_PROFDAY
), #PROFILE DAY day associated with profile, e.g. day of dose administration
PROFTIME = NOMTIME - (PROFDAY - 1)*24, #PROFILE TIME, time associated with profile, e.g. hours post dose
LIDV = LIDV, #DEPENDENT VARIABLE column name
CENS = CENS, #CENSORING column name
CMT = CMT, #COMPARTMENT column
DOSE = DOSE, #DOSE column here (numeric value)
TRTACT = TRTACT, #DOSE REGIMEN column here (character, with units),
LIDV_NORM = LIDV/DOSE,
LIDV_UNIT = EVENTU,
DAY_label = paste("Day", PROFDAY)
)
#create a factor for the treatment variable for plotting
pkpd_data = pkpd_data %>%
arrange(DOSE) %>%
mutate(TRTACT_low2high = factor(TRTACT, levels = unique(TRTACT)),
TRTACT_high2low = factor(TRTACT, levels = rev(unique(TRTACT)))) %>%
select(-TRTACT)
#create pk dataset
pk_data <- pkpd_data %>%
filter(CMT == PK_CMT)
#perform NCA, for additional plots
NCA = pk_data %>%
group_by(ID, DOSE) %>%
filter(!is.na(LIDV)) %>%
summarize(AUC_0 = ifelse(length(LIDV[NOMTIME > 0 & NOMTIME <= TAU]) > 1,
caTools::trapz(TIME[NOMTIME > 0 & NOMTIME <= TAU],
LIDV[NOMTIME > 0 & NOMTIME <= TAU]),
NA),
Cmax_0 = ifelse(length(LIDV[NOMTIME > 0 & NOMTIME <= TAU]) > 1,
max(LIDV[NOMTIME > 0 & NOMTIME <= TAU]),
NA),
AUC_tau = ifelse(length(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]) > 1,
caTools::trapz(TIME[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)],
LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]),
NA),
Cmax_tau = ifelse(length(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]) > 1,
max(LIDV[NOMTIME > (SS_PROFDAY-1)*24 &
NOMTIME <= ((SS_PROFDAY-1)*24 + TAU)]),
NA),
AUC_acc = AUC_tau / AUC_0,
Cmax_acc = Cmax_tau / Cmax_0,
SEX = SEX[1], #this part just keeps the SEX and WEIGHTB covariates
WEIGHTB = WEIGHTB[1]) %>%
gather(PARAM, VALUE, -c(ID, DOSE, SEX, WEIGHTB)) %>%
ungroup() %>%
mutate(VALUE_NORM = VALUE/DOSE,
PROFDAY = ifelse(PARAM %in% c("AUC_0", "Cmax_0"), 1, SS_PROFDAY),
DAY_label = paste("Day", PROFDAY))
#units and labels
time_units_dataset = "hours"
time_units_plot = "days"
trtact_label = "Dose"
dose_units = unique((pkpd_data %>% filter(CMT == DOSE_CMT) )$LIDV_UNIT) %>% as.character()
dose_label = paste0("Dose (", dose_units, ")")
conc_units = unique(pk_data$LIDV_UNIT) %>% as.character()
conc_label = paste0("Concentration (", conc_units, ")")
concnorm_label = paste0("Normalized Concentration (", conc_units, ")/", dose_units)
AUC_units = paste0("h.", conc_units)
#directories for saving individual graphs
dirs = list(
parent_dir = "Parent_Directory",
rscript_dir = "./",
rscript_name = "Example.R",
results_dir = "./",
filename_prefix = "",
filename = "Example.png")</code></pre>
</div>
<div id="provide-an-overview-of-the-data" class="section level2">
<h2>Provide an overview of the data</h2>
<!--START_EXPLANATION-->
<p>Summarize the data in a way that is easy to visualize the general
trend of PK over time and between doses. Using summary statistics can be
helpful, e.g. Mean +/- SE, or median, 5th & 95th percentiles.
Consider either coloring by dose or faceting by dose. Depending on the
amount of data one graph may be better than the other.</p>
<p>When looking at summaries of PK over time, there are several things
to observe. Note the number of doses and number of time points or
sampling schedule. Observe the overall shape of the average profiles.
What is the average Cmax per dose? Tmax? Does the elimination phase
appear to be parallel across the different doses? Is there separation
between the profiles for different doses? Can you make a visual estimate
of the number of compartments that would be needed in a PK model?
<!--END_EXPLANATION--></p>
<div id="concentration-over-time-colored-by-dose-mean---95-ci"
class="section level3">
<h3>Concentration over Time, colored by dose, mean +/- 95% CI</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data,
aes(x = NOMTIME, y = LIDV,
group = interaction(TRTACT_high2low, PROFDAY), color = TRTACT_high2low))
gg <- gg + xgx_theme()
gg <- gg + xgx_geom_ci(conf_level = 0.95, alpha = 0.5)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = conc_label, color = trtact_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-3-1.png" width="960" /></p>
</div>
<div
id="side-by-side-comparison-of-first-administered-dose-and-steady-state"
class="section level3">
<h3>Side-by-side comparison of first administered dose and steady
state</h3>
<!--START_EXPLANATION-->
<p>For multiple dose studies, zoom in on key visits for a clearer
picture of the profiles. Look for accumulation (if any) between first
administered dose and steady state. <!--END_EXPLANATION--></p>
<pre class="r"><code>data_to_plot <- pk_data %>% subset(PROFDAY %in% c(1, SS_PROFDAY))
gg %+%
data_to_plot %+%
aes(x = PROFTIME) +
facet_grid(~DAY_label) +
xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = "Hours")</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-4-1.png" width="960" /></p>
</div>
<div
id="concentration-over-time-faceted-by-dose-mean---95-ci-overlaid-on-gray-spaghetti-plots"
class="section level3">
<h3>Concentration over Time, faceted by dose, mean +/- 95% CI, overlaid
on gray spaghetti plots</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data, aes(x = NOMTIME, y = LIDV, group = interaction(TRTACT_high2low, PROFDAY)))
gg <- gg + xgx_theme()
gg <- gg + geom_line(aes(group = interaction(ID, PROFDAY)), color = rgb(0.5, 0.5, 0.5), size = 1, alpha = 0.3)
gg <- gg + geom_point(aes(color = factor(CENS), shape = factor(CENS), alpha = 0.3), size = 2, alpha = 0.3)
gg <- gg + scale_shape_manual(values = c(19, 8))
gg <- gg + scale_color_manual(values = c("grey50", "red"))
gg <- gg + theme(legend.position = "none")
gg <- gg + xgx_geom_ci(conf_level = 0.95)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = conc_label, color = trtact_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
gg <- gg + facet_grid(.~TRTACT_low2high)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-5-1.png" width="960" /></p>
</div>
</div>
<div id="explore-variability" class="section level2">
<h2>Explore variability</h2>
<!--START_EXPLANATION-->
<p>Use spaghetti plots to visualize the extent of variability between
individuals. The wider the spread of the profiles, the higher the
between subject variability. Distinguish different doses by color, or
separate into different panels. If coloring by dose, do the individuals
in the different dose groups overlap across doses? Dose there seem to be
more variability at higher or lower concentrations?
<!--END_EXPLANATION--></p>
<div
id="concentration-over-time-colored-by-dose-dots-and-lines-grouped-by-individual"
class="section level3">
<h3>Concentration over Time, colored by dose, dots and lines grouped by
individual</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data, aes(x = TIME, y = LIDV))
gg <- gg + xgx_theme()
gg <- gg + geom_line(aes(group = interaction(ID, PROFDAY), color = TRTACT_high2low), size = 1, alpha = 0.5)
gg <- gg + geom_point(aes(alpha = 0.3, color = TRTACT_high2low), size = 2, alpha = 0.5)
gg <- gg + geom_point(aes(shape = factor(CENS), alpha = 0.3), size = 2, alpha = 0.5, color = "red")
gg <- gg + scale_shape_manual(values = c(NA, 8))
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = conc_label, color = trtact_label, shape = "BLQ")
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-6-1.png" width="960" /></p>
</div>
<div
id="side-by-side-comparison-of-first-administered-dose-and-steady-state-1"
class="section level3">
<h3>Side-by-side comparison of first administered dose and steady
state</h3>
<pre class="r"><code>gg %+%
(pk_data %>% subset(PROFDAY %in% c(1, SS_PROFDAY))) %+%
aes(x = PROFTIME) +
xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = "Hours") +
facet_grid(~DAY_label)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-7-1.png" width="960" /></p>
</div>
<div
id="concentration-over-time-faceted-by-dose-lines-grouped-by-individual"
class="section level3">
<h3>Concentration over Time, faceted by dose, lines grouped by
individual</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data, aes(x = TIME, y = LIDV))
gg <- gg + xgx_theme()
gg <- gg + geom_line(aes(group = interaction(ID, PROFDAY)), size = 1, alpha = 0.5)
gg <- gg + geom_point(data = pk_data %>% filter(CENS == 0), size = 2, alpha = 0.5)
gg <- gg + geom_point(data = pk_data %>% filter(CENS == 1), size = 2, alpha = 0.5, color = "red", shape = 8)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = conc_label)
gg <- gg + facet_grid(.~TRTACT_low2high)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-8-1.png" width="960" /></p>
</div>
</div>
<div id="assess-the-dose-linearity-of-exposure" class="section level2">
<h2>Assess the dose linearity of exposure</h2>
<div
id="dose-normalized-concentration-over-time-colored-by-dose-mean---95-ci"
class="section level3">
<h3>Dose Normalized Concentration over Time, colored by dose, mean +/-
95% CI</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data,
aes(x = NOMTIME, y = LIDV_NORM,
group = TRTACT_high2low, color = TRTACT_high2low))
gg <- gg + xgx_theme()
gg <- gg + xgx_geom_ci(conf_level = 0.95, alpha = 0.5)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = concnorm_label, color = trtact_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-9-1.png" width="960" /></p>
</div>
<div
id="side-by-side-comparison-of-first-administered-dose-and-steady-state-2"
class="section level3">
<h3>Side-by-side comparison of first administered dose and steady
state</h3>
<pre class="r"><code>gg %+%
(pk_data %>% subset(PROFDAY %in% c(1, SS_PROFDAY))) %+%
aes(x = PROFTIME) +
xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = "Hours") +
facet_grid(~DAY_label) +
xlab("Time after dose (hours)") </code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-10-1.png" width="960" /></p>
</div>
<div id="nca-of-dose-normalized-auc-vs-dose" class="section level3">
<h3>NCA of dose normalized AUC vs Dose</h3>
<!--START_EXPLANATION-->
<p>Observe the dose normalized AUC over different doses. Does the
relationship appear to be constant across doses or do some doses stand
out from the rest? Can you think of reasons why some would stand out?
For example, the lowest dose may have dose normalized AUC much higher
than the rest, could this be due to CENS observations? If the highest
doses have dose normalized AUC much higher than the others, could this
be due to nonlinear clearance, with clearance saturating at higher
doses? If the highest doses have dose normalized AUC much lower than the
others, could there be saturation of bioavailability, reaching the
maximum absorbable dose? <!--END_EXPLANATION--></p>
<pre class="r"><code>gg <- ggplot(data = NCA %>% subset(PARAM %in% c("AUC_0", "AUC_tau")), aes(x = DOSE, y = VALUE_NORM))
gg <- gg + geom_boxplot(aes(group = DOSE)) + geom_smooth(method = "lm", color = "black")
gg <- gg + scale_x_continuous(breaks = unique(NCA$DOSE))
gg <- gg + ylab("Dose normalized AUCtau (h.(ng/mL)/mg)")
gg <- gg + xlab("Dose (mg)")
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg + facet_grid(.~DAY_label))</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-11-1.png" width="768" /></p>
<!--START_EXPLANATION-->
<p>For multiple ascending dose studies, plot the ratio between the
steady state AUC and the AUC from the first dose administration. Is
there any accumulation? <!--END_EXPLANATION--></p>
<pre class="r"><code>gg %+%
(NCA %>% subset(PARAM == "AUC_acc")) +
aes(y = VALUE) +
geom_hline(yintercept = 1, linetype = "dashed") +
xgx_scale_y_log10(breaks = c(0.5, 1, 2)) +
coord_cartesian(ylim = c(0.5, 3)) +
ylab("Fold Accumulation in AUCtau")</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-12-1.png" width="768" /></p>
</div>
<div id="nca-of-dose-normalized-cmax-vs-dose" class="section level3">
<h3>NCA of dose normalized Cmax vs Dose</h3>
<pre class="r"><code>gg %+%
(NCA %>% subset(PARAM %in% c("Cmax_0", "Cmax_tau"))) +
facet_grid(.~DAY_label) +
ylab("Dose normalized Cmax (ng/mL)/mg") </code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-13-1.png" width="768" />
<!--START_EXPLANATION--> For multiple ascending dose studies, plot the
ratio between the steady state Cmax and the Cmax from the first dose
administration. Is there any accumulation? <!--END_EXPLANATION--></p>
<pre class="r"><code>gg %+%
(NCA %>% subset(PARAM == "Cmax_acc")) +
aes(y = VALUE) +
geom_hline(yintercept = 1, linetype = "dashed") +
xgx_scale_y_log10(breaks = c(0.5, 1, 2)) +
coord_cartesian(ylim = c(0.25, 4)) +
ylab("Fold Accumulation in Cmax")</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-14-1.png" width="768" /></p>
</div>
</div>
<div id="explore-irregularities-in-profiles" class="section level2">
<h2>Explore irregularities in profiles</h2>
<!--START_EXPLANATION-->
<p>Plot individual profiles in order to inspect them for any
irregularities. Inspect the profiles for outlying data points that may
skew results or bias conclusions. Looking at the shapes of the
individual profiles now, do they support your observations made about
the mean profile (e.g. number of compartments, typical Cmax, Tmax)?</p>
<p>Plotting individual profiles on top of gray spaghetti plots puts
individual profiles into context, and may help identify outlying
individuals for further inspection. Are there any individuals that
appear to have very high or low Cmax compared to others within the same
dose group? What about the timing of Cmax? What about the slope of the
elimination phase? Does it appear that any subjects could have received
an incorrect dose? <!--END_EXPLANATION--></p>
<div
id="concentration-over-time-faceted-by-individual-individual-line-plots-overlaid-on-gray-spaghetti-plots-for-that-dose-group"
class="section level3">
<h3>Concentration over Time, faceted by individual, individual line
plots overlaid on gray spaghetti plots for that dose group</h3>
<pre class="r"><code># In order to plot grayed out spaghetti plots by dose with individuals highlighted,
# a new dataset needs to be defined that groups the individuals by dose
pk_data_rep_by_trt <- list()
for(id in unique(pk_data$ID)){
indiv_data <- pk_data %>% subset(ID == id)
itrtact = unique(indiv_data$TRTACT_low2high)
pk_data_rep_by_trt[[as.character(id)]] <- pk_data %>%
subset(TRTACT_low2high == itrtact) %>%
mutate(ID_rep_by_trt = ID, ID = id)
}
pk_data_rep_by_trt <- bind_rows(pk_data_rep_by_trt)
gg <- ggplot(mapping = aes(x = TIME, y = LIDV))
# Plot gray underlaid spaghettis
gg <- gg + geom_line(data = pk_data_rep_by_trt,
aes(group = interaction(ID_rep_by_trt, PROFDAY)),
size = 1, color = rgb(0.5, 0.5, 0.5), alpha = 0.3)
gg <- gg + geom_point(data = pk_data_rep_by_trt,
size = 1, color = rgb(0.5, 0.5, 0.5), alpha = 0.3)
# plot overlaid individuals
gg <- gg + geom_line(data = pk_data,
aes(group = interaction(ID, PROFDAY)), size = 1)
gg <- gg + geom_point(data = pk_data %>% filter(CENS == 0), size = 1)
gg <- gg + geom_point(data = pk_data %>% filter(CENS == 1),
color = "red", shape = 8, size = 2)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + labs(y = conc_label)
gg <- gg + theme(legend.position = "none")
gg <- gg + facet_wrap(~ID + TRTACT_low2high,
ncol = 10 )
gg <- gg + xgx_annotate_status(status, fontsize = 4, color = rgb(0.5, 0.5, 1))
gg <- gg + xgx_annotate_filenames(dirs)
gg <- gg + theme(panel.grid.minor.x = ggplot2::element_line(color = rgb(0.9,0.9,0.9)),
panel.grid.minor.y = ggplot2::element_line(color = rgb(0.9,0.9,0.9)))
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg)</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-15-1.png" width="768" /></p>
</div>
</div>
<div id="explore-covariate-effects-on-pk" class="section level2">
<h2>Explore covariate effects on PK</h2>
<!--START_EXPLANATION-->
<p>See <a
href="Single_Ascending_Dose_PK.html#explore_covariate_effects_on_pk">Single
Ascending Dose PK</a> <!--END_EXPLANATION--></p>
<div
id="concentration-over-time-colored-by-categorical-covariate-mean---95-ci"
class="section level3">
<h3>Concentration over Time, colored by categorical covariate, mean +/-
95% CI</h3>
<pre class="r"><code>gg <- ggplot(data = pk_data, aes(x = NOMTIME, y = LIDV, color = SEX))
gg <- gg + xgx_theme()
gg <- gg + xgx_geom_ci(conf_level = 0.95)
gg <- gg + xgx_scale_y_log10()
gg <- gg + xgx_scale_x_time_units(units_dataset = time_units_dataset,
units_plot = time_units_plot)
gg <- gg + ylab(conc_label)
gg <- gg + xgx_annotate_status(status)
gg <- gg + xgx_annotate_filenames(dirs)
#if saving copy of figure, replace xgx_annotate lines with xgx_save() shown below:
#xgx_save(width, height, dirs, "filename_main", status)
print(gg + facet_grid(.~TRTACT_low2high))</code></pre>
<p><img src="Multiple_Ascending_Dose_PK_files/figure-html/unnamed-chunk-16-1.png" width="960" /></p>