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Instructions for examination project
Frame story
The active period of the study is finally in! You have previously worked with the run-in data of this study, where you found that the CD4 count was depending on the health status (HIV versus HIV/TB), but the CD4 count did not change over time during the run-in. The study design was double-blinded, randomized with 100 subjects receiving 200 mg of study compound (BPI889) as add-on to current standard of care treatment.
Blood samples were collected at 10 min, 20 min, 30 min, 45 min, 1, 1.5, 2, 2.5 3, 4, 5, 6, 8, 12 and 24 h post dose for BPI889 concentration determination. You will before the initial pharmacokinetic (PK) analysis of BPI889. The PK data is found in BPI889_PK_XX.csv (on the student portal: R examination Project/PK_data/)
When investigating the PK of a compound the following indices are usually derived: half-life (t1/2), maximum concentration (Cmax), volume of distribution (Vd), area under the concentration-time curve (AUC) and clearance (CL). Details about how to retrieve these indices are given in supplementary material A. These indices are derived on an individual level.
Based on results of previous studies and pre-clinical testing, the ADME (absorption, distribution, metabolism and excretion) group suspects that the PK is related to body size and body composition. Measurements used to assess body composition are: total body water (TBW) and lean body mass (LBM). Details about how to calculate these measurements are given in supplementary material B.
In vitro experiments have revealed that cytochrome P450 enzymes, in particular CYP2D6, is likely to be involved in the elimination of BPI889. The clinical team is concerned that variants of the enzyme will affect PK. To investigate this, genetic testing was performed on all subjects in the study. Five single nucleotide polymorphisms (SNPs), previously found to affect the function of CYP2D6, were tested: T134A, A443G, G769C, G955C and A990C. The genetic data is found in BP1889_genetics_XX.txt (on the student portal: R examination Project/SNP_data/). In this file, subjects are classified as wildtype (0 in the data), heterogeneous variant (1 in the data) and homogenous variant (2 in the data) for each potential SNP.
Apart from the files with PK and SNP data, a R-markdown template has been uploaded on the student portal (R examination Project/project_examination_template.Rmd). It is optional if you wish to use R-markdown for the assignment or if you prefer to do it with an R-script.
Your main task is to investigate if any of the five SNPs of genes coding for the enzyme CYP2D6 and body composition significantly affects the PK of BPI889.
Examination project
TaskS
The following parts are expected to be present in the R-script/Rmd-file that is submitted:
1.Correctly import data files on computer as objects in R
oComment about what arguments you choose to set and why
2.Assign appropriate names and classes to variables
oComment about why/what you have done
3.Combine two objects (data.frames with PK- and SNP-data) into one object with long format
4.Calculate body composition measurement: LBM
5.Categorize LBM into two groups: high or low (being above or below 50 kg)
6.Calculate PK variables: Cmax, t1/2 and AUC
7.Numerically summarise t1/2 and AUC
8.Graphically display PK versus time with a spaghetti plot
9.Graphically display correlations between t1/2 and AUC
10.Graphically display AUC versus SNPs as boxplots to assess a relationship
11.Graphically display correlations between t1/2 and LBM to assess a relationship and add a linear regression
12.Statistically test (ANOVA) the relationship of AUC and Cmax versus SNPs
13.Statistically test (t-test) the relationship between t1/2 and the categorical LBM
The project is an individual task. Plagiarism is not allowed. If you re-used code you found online, please indicate the original source in a comment.
Report and grade
The report will be graded based on the code in the submitted script. The script completing all tasks should be uploaded to the student portal before the deadline (see student portal).
Minimum requirements to pass are that the script must 1) contain code for all above listed tasks, 2) run on the examiner’s computer after adapting paths to files on the current system, and 3) follow the rules of pretty scripting.
A simple and avoidable error is that the script references objects that were created outside the script and thus will not run on a new system. Please, check this carefully before submitting your script.
Feedback will be given on the student portal after deadline and one resubmission with corrected report is possible within the current course. Projects, graded “fail” after the second submission, are referred to the next time the course is given (HT-2020) for additional submissions.
If you fail to submit a script before the first deadline, you are still eligible for submission at the second deadline, but will not be allowed an additional resubmission within the course, and additional submissions are referred to the next course.
A higher grade (pass with excellence) will be given to those scripts that show high readability (follows good coding practise), solutions avoiding redundant code and usage of existing packages. Additionally, general solutions, as opposed to specific solutions will be rated higher, as will simplicity of code, over complexity.

Supplementary material A – Pharmacokinetic indices

Parameter Description and calculation
t1/2 Half-life describes the time it takes to reduce the concentration to half:

k is derived as the absolute value (i.e. always positive) of the slope of the terminal phase of the ln(C) versus time curve according to the equation of linear relationships:

Cmax Maximum concentration is the maximum observed concentration and is predictive of the expected maximum effect of the substance. Unit: mg/L
Vd Volume of distribution describes the apparent volume that dose distributes in at time=0 to give C0. Vd is derived from

where C0 is derived from the same equation as k:

AUC Area under concentration-time curve can be derived by the trapezoidal rule:

where Cn is the last observations and k is described in the section of t1/2.

CL Clearance describes the capacity of the body to clear a volume of plasma entirely from the compound during a certain time period.
Calculated as:
Supplementary material B – Measurements of bodY composition

Parameter Description and calculation
TBW Total body water is calculated as:

TBW in L, age in years, height in cm and weight in kg.
LBM Lean body mass is calculated as:

LBM in kg, weight in kg and height in cm.

 

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