Analgesics

Methods for Data Visuals

Pharmacogentics is the study of how genetic biomarkers affect the pharmacology of pharmaceuticals. This emerging field is of considerable importance in the field of drug discovery and health informatics. One technique to better understand and systematize pharmacogentic data is to use Business Intelligence tools to classify which pharmaceuticals affect key enzyme pathways, which diseases they are used to treat, and how to better target forthcoming research on reducing patient toxicity. The trends of genomics research will allow for a more thorough system of personalized medicine to emerge; immune, gastrointestinal, endocrine, and neural therapeutics may become safer to consume as a result.

Looking at relevant genes and pharmaceuticals with certain associations with the gene are a crucial first step to better understanding how drug metabolism varies among a sample population. Take a look at the chart below, created in Excel, to show data from PharmaGKB with “well-known pharmacogenomic information”:

Row Labels Count of Drug
CYP2C19 49
CYP2C9 44
CYP2D6 76
CYP3A4 95
CYP3A5 13
F2 3
F5 6
MTHFR 2
VKORC1 3
Grand Total 291

 

Now let’s scan over the pharmaceuticals associated with some of the genes below; to simplify things only a few genes have been selected:

Row Labels Count of Drug
CYP2C19 49
CYP2C9 44
CYP2D6 76
CYP3A4 95
CYP3A5 13
F2 3
     ethinyl estradiol 1
     norelgestromin 1
     tamoxifen 1
F5 6
     drospirenone 1
     eltrombopag 1
     ethinyl estradiol 1
     hormonal contraceptives for systemic     use 1
     norelgestromin 1
     tamoxifen 1
MTHFR 2
     ethinyl estradiol 1
     norelgestromin 1
VKORC1 3
     acenocoumarol 1
     phenprocoumon 1
     warfarin 1
Grand Total 291

After taking a look at the pharmaceuticals associated with particular genes, it becomes possible to decipher patterns of information based on the types of therapeutics fall in each gene. If we are to build better associations of therapeutics and their pharmacogenomic implications, it may be beneficial to understand how different receptors are associated with certain genetic biomarkers.

For example, if Tamoxifen in the list above is present under the genes F2 and F5, then we could do a further analysis into the pharmacology of Tamoxifen. It is known that Tamoxifen has an affinity for Estrogen receptors. Therefore, there may be a role that genes F2 and F5 play in certain nervous system functions that affect certain brain regions (such as the hypothalamus and neurohormone secretion which may lead to certain neuroimmune responses).

If we can build better models to quantify drug metabolism then personalized medicine will be more feasible in the short-term.

For more information and discussion on this post, email me at will[at]mindbodymetrics.com

Here’s a word tree I created with Google Charts to better visualize analgesics (pain relievers) based on their mechanism of action:
Analgesics_

A Part 2 Post on Pharmacogenomics is forthcoming with more details on data visualization tools to building relationships between particular genes, pharmacology, and classes of pharmaceuticals.

Part 2 will show some methods to finding patterns in the table below:

Row Labels Count of Drug
CYP2C19 49
abiraterone 1
afatinib 1
amitriptyline 1
arformoterol 1
atazanavir 1
axitinib 1
carisoprodol 1
citalopram 1
clobazam 1
clomipramine 1
clopidogrel 1
crizotinib 1
dabrafenib 1
dasatinib 1
dexlansoprazole 1
dextromethorphan 1
diazepam 1
doxepin 1
drospirenone 1
eltrombopag 1
escitalopram 1
esomeprazole 1
ethinyl estradiol 1
fluvoxamine 1
imipramine 1
lansoprazole 1
letrozole 1
lomitapide 1
moclobemide 1
modafinil 1
nelfinavir 1
omeprazole 1
pantoprazole 1
phenytoin 1
prasugrel 1
propranolol 1
quinidine 1
quinine 1
rabeprazole 1
regorafenib 1
Selective serotonin reuptake inhibitors 1
sertraline 1
simeprevir 1
ticagrelor 1
tolterodine 1
trimipramine 1
venlafaxine 1
voriconazole 1
vortioxetine 1
CYP2C9 44
abiraterone 1
acenocoumarol 1
afatinib 1
anastrozole 1
capecitabine 1
carvedilol 1
celecoxib 1
ceritinib 1
citalopram 1
clobazam 1
clopidogrel 1
crizotinib 1
dabrafenib 1
dasatinib 1
dexlansoprazole 1
dextromethorphan 1
diazepam 1
drospirenone 1
eltrombopag 1
esomeprazole 1
ethinyl estradiol 1
flurbiprofen 1
fluvoxamine 1
glibenclamide 1
gliclazide 1
glimepiride 1
modafinil 1
phenprocoumon 1
phenytoin 1
prasugrel 1
quinidine 1
quinine 1
regorafenib 1
rosuvastatin 1
ruxolitinib 1
sildenafil 1
sulfamethoxazole 1
tolbutamide 1
tolterodine 1
trimethoprim 1
venlafaxine 1
voriconazole 1
vortioxetine 1
warfarin 1
CYP2D6 76
abiraterone 1
acetaminophen 1
amitriptyline 1
arformoterol 1
aripiprazole 1
atomoxetine 1
carvedilol 1
celecoxib 1
cevimeline 1
citalopram 1
clobazam 1
clomipramine 1
clozapine 1
codeine 1
crizotinib 1
darifenacin 1
dasatinib 1
desipramine 1
dexlansoprazole 1
dextromethorphan 1
diazepam 1
doxepin 1
dronedarone 1
drospirenone 1
duloxetine 1
eliglustat 1
esomeprazole 1
ethinyl estradiol 1
everolimus 1
fesoterodine 1
flecainide 1
fluoxetine 1
flupenthixol 1
fluvoxamine 1
fosamprenavir 1
galantamine 1
gefitinib 1
haloperidol 1
ibrutinib 1
iloperidone 1
imatinib 1
imipramine 1
metoprolol 1
mirtazapine 1
modafinil 1
nefazodone 1
nortriptyline 1
olanzapine 1
oxycodone 1
paroxetine 1
pazopanib 1
perphenazine 1
pimozide 1
ponatinib 1
propafenone 1
propranolol 1
protriptyline 1
quinidine 1
quinine 1
ranolazine 1
risperidone 1
ritonavir 1
Selective serotonin reuptake inhibitors 1
tamoxifen 1
terbinafine 1
tetrabenazine 1
thioridazine 1
timolol 1
tiotropium 1
tipranavir 1
tolterodine 1
tramadol 1
trimipramine 1
venlafaxine 1
vortioxetine 1
zuclopenthixol 1
CYP3A4 95
abiraterone 1
acetaminophen 1
afatinib 1
anastrozole 1
aripiprazole 1
atazanavir 1
atorvastatin 1
axitinib 1
boceprevir 1
bosutinib 1
brentuximab vedotin 1
cabazitaxel 1
carbamazepine 1
ceritinib 1
cevimeline 1
citalopram 1
clobazam 1
clozapine 1
codeine 1
crizotinib 1
dabrafenib 1
darifenacin 1
darunavir 1
dasatinib 1
dexlansoprazole 1
dextromethorphan 1
diazepam 1
dronedarone 1
drospirenone 1
efavirenz 1
eliglustat 1
eltrombopag 1
emtricitabine 1
erlotinib 1
esomeprazole 1
ethinyl estradiol 1
everolimus 1
exemestane 1
fentanyl 1
fesoterodine 1
fluvoxamine 1
fosamprenavir 1
fulvestrant 1
gefitinib 1
iloperidone 1
imatinib 1
indacaterol 1
indinavir 1
irinotecan 1
ivabradine 1
ivacaftor 1
lapatinib 1
letrozole 1
lomitapide 1
maraviroc 1
modafinil 1
nefazodone 1
nelfinavir 1
nilotinib 1
omeprazole 1
pantoprazole 1
pazopanib 1
pimozide 1
ponatinib 1
posaconazole 1
prasugrel 1
propafenone 1
quinidine 1
quinine 1
ranolazine 1
regorafenib 1
ritonavir 1
rosuvastatin 1
ruxolitinib 1
sildenafil 1
simeprevir 1
sirolimus 1
sunitinib 1
telaprevir 1
telithromycin 1
tenofovir 1
ticagrelor 1
tiotropium 1
tipranavir 1
tolterodine 1
tramadol 1
trametinib 1
trastuzumab emtansine 1
vandetanib 1
vardenafil 1
vemurafenib 1
venlafaxine 1
voriconazole 1
vortioxetine 1
zonisamide 1
CYP3A5 13
abiraterone 1
axitinib 1
boceprevir 1
brentuximab vedotin 1
crizotinib 1
ivacaftor 1
maraviroc 1
ponatinib 1
prasugrel 1
tacrolimus 1
ticagrelor 1
trastuzumab emtansine 1
vortioxetine 1
F2 3
ethinyl estradiol 1
norelgestromin 1
tamoxifen 1
F5 6
drospirenone 1
eltrombopag 1
ethinyl estradiol 1
hormonal contraceptives for systemic use 1
norelgestromin 1
tamoxifen 1
MTHFR 2
ethinyl estradiol 1
norelgestromin 1
VKORC1 3
acenocoumarol 1
phenprocoumon 1
warfarin 1
Grand Total 291

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