Chapter 1

Question 1

Describe the difference between ‘long’ and ‘wide’ data formats and state which is most commonly used in pharmacokinetic modelling. (3 marks)

Answer 1

In long format data each row represents an observation for each ID, whereas in wide format each row represents a single ID and the columns contain repeated measures. Multiple time points appear horizontally as separate columns.
Long format is most commonly used in pharmacokinetic modelling, as it is easier for computer programmes like R to analyse and manipulate the data. Because it allows the data to be organised with repeated measures over time. In long form each event for each ID has its own row.

Question 2

Give an example line of code for - Multiplication

Answer 2

3*2

Question 3

Give an example line of code for each of - Addition

Answer 3

2+4 #Addition

Question 4

Give an example line of code for each of - Natural logarithm

Answer 4

log(2)

Question 5

What does the command ‘header=TRUE’ indicate

Answer 5

indicates the first row of the csv file contains headers(labels) for the variable

Question 6

You have prepared a data frame that you would like to save. You are not sure that R is working in the correct folder that you wish to save it into.
What command will allow you to check that R is working in the correct folder?

Answer 6

getwd()

Question 7

How to check the contents of the working directory in R

Answer 7

list.files()

Question 8

What does the function head() do?

Answer 8

Shows the first 6 rows of the data frame

Question 9

data[15,]

Answer 9

looks at row 15

Question 11

How to use R to check for anomalies:

Answer 11

summary () function

Question 12

What are R packages

Answer 12

groups of functions. Packages bring in new functions to use with/for the data (datasets).

Question 13

what is %>%

Answer 13

Piping (%>%) is a tool that enables multiple logical steps to achieve a desired output.

Question 14

What line of code should be added to plot a solid line for each individual?
A) geom_line(by=ID)
B) ggplot(data=data, aes(x=time, y=conc, group=ID))
C) ggplot(data=data, aes(x=time, y=conc, group_by=ID))
D) ggplot(data=data, aes(x=time, y=conc, line=ID))
E) geom_line(aes(linetype=ID))

Answer 14

B) ggplot(data=data, aes(x=time, y=conc, group=ID))

Question 15

What command would produce a separate panel for each individual?
A) facet_wrap(vars(ID))
B) geom_line(aes(col=ID))
C) ggplot(data=data, aes(x=time, y=conc, fill=ID))
D) ggplot(data=data, aes(x=time, y=conc, line=ID))
E) geom_line(col=ID)

Answer 15

facet_wrap(vars(ID))

Question 16

Why is the function ‘==’ used

Answer 16

it is needed when using code to look for something being exactly a specific value.
You need 2 because it then allows for reasonable alternatives like >=.

Question 17

τ vs T

Answer 17

τ refers to a dosing interval
AUC_(0-τ) is the AUC from time = 0 to time = τ (τ is just before the next dose is given/0

whereas AUC_(0-T)is any time duration (usually duration of the study)

Question 18

How to isolate a participant in a dataset?

Answer 18

Aamox<-amox%>%
subset(ID==’A’)

Question 19

What is the advantage of the logarithmic over the linear method in estimating AUC?
Improved accuracy of AUC estimation for absorption phase
Improved accuracy of AUC estimation
Improved accuracy of AUC estimation in elimination phase
Improved accuracy of AUC estimation at steady state
Improved accuracy of AUC estimation for a continuous infusion

Answer 19

Improved accuracy of AUC estimation in elimination phase

This choice is correct because the logarithmic trapezoidal method more accurately models the exponential decay associated with drug elimination from the body. Unlike the linear trapezoidal method, which assumes a straight-line change between points and can overestimate AUC during rapid changes in concentration, the logarithmic method aligns better with the natural log-linear pharmacokinetics seen during the elimination phase, providing a more precise estimate of AUC under these conditions.

Question 20

At steady state AUC =

Answer 20

AUC_(0-τ)=AUC_(0-∞)

Question 21

Define Bioavailability (F)

Answer 21

Fraction of dose that reaches the systemic circulation unchanged

Question 22

Formula for estimating bioavailability

Answer 22

F= 〖AUC〗(oral,0-∞)/〖AUC〗(iv,0-∞)

Question 23

how to add a new column(variable) when undertaking non-compartmental analysis (PKNCA)

Answer 23

use %>% and mutate() function

damox<-amox%>% #save this as a new object
subset (time==0)%>%
mutate(dose=1000) #this produces a new variable dose = 1000

Question 24

estimating clearance using AUC0−∞

Answer 24

CL = Dose/〖AUC〗_(0-∞)

Question 25

Define clearance

Answer 25

The volume of plasma cleared entirely of drug per unit time (m/min)

Question 26

State the benefits of non-compartmental PK analysis

Answer 26

They use relatively straightforward algebra with few assumptions about the data to calculate summary pharmacokinetic information. Most regulators will require non-compartmental analysis of data as part of regulatory submissions. Simplicity: Does not require assumptions about the number of compartments. Broad Applicability: Useful for initial PK analysis and comparison between different conditions or treatments. Quick Results: Less computationally intensive and easier to interpret.

Question 27

State the limitations of non-compartmental PK analysis

Answer 27

Need for rich sampling (multiple samples from each subject) Use of direct observation of C~max and T~max means true values are missed for any particular subject. Increasing accuracy requires increased number of participants or samples, which can be technically challenging and expensive. Impractical Limitations: Less Specific: Does not provide insights into the biological mechanisms of drug distribution and elimination. Assumes Steady-State: Most effective when the system is at a steady state, which is not always the case. Sensitivity: May be less accurate with sparse data or irregular sampling.

Question 28

What best describes the relationship between dose, clearance and AUC following a single dose of a drug?

Answer 28

CL=Dose/(AUC_(0-∞) )

Question 29

In what scenario does AUC_(0-τ)=AUC_(0-∞)?

Answer 29

At steady state

Question 30

Give the common pharmacokinetic definition for AUC

Answer 30

AUC (Area Under the Concentration-Time Curve) reflects the total exposure to the drug.

Question 31

Give the common pharmacokinetic definitions for Cmax and Tmax

Answer 31

C_max is the maximum concentration T_max time of the maximum concentration

Question 32

Give the common pharmacokinetic definition for τ

Answer 32

dosing interval

Question 33

Comparison of Linear Trapezoidal Method with the Logarithmic Trapezoidal Method:

Answer 33

Logarithmic Trapezoidal Method: is used when the drug concentration decreases exponentially (typically during the elimination phase). Differences: The linear method assumes a constant rate of change between two time points and is best used when the concentration decreases linearly. The logarithmic method, however, assumes a logarithmic decrease, providing more accuracy in cases where drug elimination follows first-order kinetics.

Question 35

Describe three advantages and three disadvantages of non-compartmental pharmacokinetic analysis. (3 marks)

Answer 35

Advantages of Non-Compartmental Analysis (NCA): Simplicity: NCA does not require assumptions about the number of compartments or the underlying biological processes, making it straightforward and easier to understand compared to compartmental models. Broad Applicability: It can be used in a wide range of clinical and preclinical studies to provide quick estimates of key pharmacokinetic parameters, such as clearance (CL), volume of distribution (Vd), and half-life (t 1 / 2 1/2 ​ ). Speed and Efficiency: Since NCA calculations are less computationally intensive, results can be obtained quickly, which is beneficial during early-stage drug development and initial pharmacokinetic analysis. Disadvantages of Non-Compartmental Analysis: Lack of Mechanistic Insight: NCA provides limited information about the biological mechanisms influencing drug absorption, distribution, and elimination due to the absence of compartment modeling. Assumptions of Linear Kinetics: NCA generally assumes that the pharmacokinetics of a drug are linear (dose-proportional), which may not hold true for drugs exhibiting non-linear behavior. Sensitivity to Sampling Strategy: The accuracy of NCA depends on the frequency and timing of data points. Sparse or poorly timed sampling can lead to errors in estimating pharmacokinetic parameters.