MATH1309 Assessing Druggability in Drug Discovery case study Questions and Answers

 

Do you seek MATH1309 Assessing Druggability in Drug Discovery case study Questions and Answers? We have Case Study Assignment Help service by professional writers with 100 % plagiarism free and high quality content at a cost effective price. If you have any query from case study related than , you can avail at QnAassignmenthelp.com.

 

 

 

Project Description:

• Drug–likeness is not a precisely defined concept in drug discovery. Predicting druggability is of high
practical relevance in pharmaceutical research. In vitro absorption, distribution, metabolism, and
elimination (ADME) assays are now being conducted throughout the drug discovery process, but
there is still a need to develop faster and better analytic methods to enhance the ‘developability’ of
drug leads, and to formalise strategies for ADME assessment of good molecular candidates in the
drug discovery and pre–clinical stages.

• This study involves 400 small molecules data retrieved from the DrugBank 3.0 database a unique
chem–informatics resource analysed by Hudson et al., (2014, 2017, 2019, 2020).

• The data set contains 9 physico–chemical variables (MW, PSA, log P, Log D, …), and the molecule’s
mode of delivery (oral versus non–oral). See Table 1 below.
                                                                                                         

                                                                                                                Table 1:

In addition, the data set contains new druggability rules (score functions counting the number of violations for each molecule on each of the 9 variables) developed by Hudson et al. These account for the molecule’s size, permeability etc., but use new cutpoints for each of 9 molecular parameters (Table 2), different to those conventionally used by the Food and Drug Advisory group (FDA) (Lipinski’s rule Ro5, Table 2).

Hudson et al based on the 9 molecular variables (ADME variables) found distinct clusters of the molecules identified as “poor” versus “good” druggables. The data set contains the 9 ADME variables (Table 1), and a scoring function (score9_LogD) along with the molecule’s mode of delivery (oral versus non–oral).

The score is denoted as score9_ LogD. Note that the function score9_LogD is a continuous variable of range 0 to 9 – comprised of the 4 traditional parameters of the rule of five (Ro5) (Lipinski, 2016) (Table 1) plus 4 extra parameters (PSA, number of rotatable bonds, rings, N and O atoms) with 2 extra candidates lipophicility, log P or logD, the latter is the distribution coefficient, recently suggested as a possible preferable predictor for permeation, preferable to Lipinski’s traditional partition coefficient, Log P, an
often used predictor for permeation.

 

We also dichotomise the score9_LogD_ into 2 groups based on the cutpoint of 4 violations:
• Cutpoint <=4 is a non–violator molecule
• Cutpoint >4 is violator (non–druggable) molecule.
This is equivalent to: Score9_Log D_group <=4 (non–violators) versus Score9_log D_group >4 (violators)

 

Table 2: Values above the cutpoints score a 1.0

 

Description of the drugbank dataset of N = 400 molecules:

 

 

 

A random sample of 12 molecules’ data is shown below as an example (this is not the full N=400
dataset):

 

 

data columns continued…