Introduction to Pharmacokinetics

What is Pharmacokinetics?

You might think this is a simple question, especially for an expert in pharmacokinetics. Well, this question is full of controversy with each scientist having their own opinion. I think this is because the definition of pharmacokinetics is simple, yet pharmacokinetics science is broad in scope.

The word pharmacokinetics is from two greek words (see wikipedia for more):

pharmakon: Drug
kineticos: to do with motion

Ideal Body Weight:
Male= 50Kgs + 2.3 kg over 5ft in height
Female=45.5kgs + 2.3 kg over 5ft in height

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Source : PK/PD Associates
Learn PK/PD

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

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The Drug Discovery Process

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Source: PhRMA Press

Clinical Trials – Overview

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Source:ClinicalTrials

Clinical Trials

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Source: Patient Education Institute

A Guide to Understanding Clinical Trials

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Source: ClinicalConnection

Acme Pharma Develops A Drug: Part I

Learn more about how the pharmaceutical industry has traditionally developed and brought drugs to market. Watch part II of this series to learn how Network Fortress can improve the drug development process and save pharma and biotech companies time and money.

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What is Clinical Reviewer?

Clinical Review on iPad

  1. CRF Images View CRF (Case Report Form) data with pinch zoom.
  2. SAS Datasets Search and view data exported from SAS datasets.
  3. DEFINE.XML Import meta data directly from DEFINE.XML.
  4. Control Terminology View all metadata including coded terms defined in DEFINE.XML
  5. Secure Data – Data transferred to local memory viewed in “Airplane” mode with self deleting expiration.

Clinical data can be imported from standard CDISC DEFINE.XML format directly onto Clinical Reviewer app on iPad. Take advantage of the multi-touch interface to view Case Report Form or SAS datasets directly. Metadata including variable and value level metadata is viewable as defined in DEFINE.XML.

Watch this tutorial and see for yourself…

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“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Source: Meta-x

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

CDISC Builder™ – Verifying your Data for CDISC Standard Compliance

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Source:Meta-X

How to Use SAS – Lesson 6 – SAS Arithmetic and Variable Creation

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 6 introduces the concept of SAS arithmetic in the DATA STEP. I discuss how one can add, subtract, divide, multiply, or create their own formulas for variables in the data. I also discuss using SAS arithmetic to create new variables based on mathematical transformations of old variables, which may sometimes aid in meeting the assumptions of statistical tests. Finally, I provide basic examples of each of these methods.

Helpful Notes:

1. SAS uses many of the same arithmetic operators to add, subtract, divide and multiply as other programming languages and basic algebra.

2. Arithmetic operations on variables affect the entire list of observations. So be careful in operating with existing variables and make new variables if you can afford to.

3. The varnum ;option on the PROC CONTENTS statement can allow you to see the variables listed in the order they were created.

Today’s Code:

data main;
input x y;
cards;
1 2
3 4
5 6
7 8
;
run;

proc print data=main;
run;

data new_main; set main;
a = x + y;
b = x – y;
c = x * y;
d = x / y;
e = x ** y;
f = ((x + y) * (x – y));
run;

proc contents data=new_main varnum;
run;

proc print data=new_main;
run;

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“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.

How to Use SAS – Lesson 5 – Data Reduction and Data Cleaning

This video series is intended to help you learn how to program using SAS for your statistical needs. Lesson 5 introduces the concept of data reduction (also known as subsetting ;data sets). I discuss how one can subset a data set (i.e. reduce a data set’s number of observations) based on some criteria using the IF statement in the DATA STEP, or using the WHERE statement in a PROC STEP. I also discuss using the KEEP, DROP, and RENAME statements for reducing data to only a handful of the original variables (i.e. reduce a data set’s number of variables). Furthermore, I show how one can label variables so that descriptive information can be presented in output and value formats so that specific values are easy to understand. Finally, I provide basic examples of each of these for three hypothetical data sets.

Helpful Notes:

1. There are two places you can reduce the data you analyze; in the DATA STEP, and in the PROC STEP.

2. To subset data in the DATA STEP, use the IF statement.

3. To subset data in the PROC STEP, use the WHERE statement.

4. Another way to reduce data is to eliminate variables using a KEEP or DROP statement. This method is useful if you are creating a second data set or analytic version of your main dataset.

5. The RENAME statement simply changes a variables name.

Today’s Code:

data main;
input x y z;
cards;
1 2 3
7 8 9
;
run;

proc contents data=main; run;
proc print data=main; run;

/* 1. Reduce data in the DATA STEP using a simple IF statement */
data reduced_main; set main;
if x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 2. Reduce data in the PROC STEP using a simple WHERE statement */
proc print data=main;
where x = 1;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 3. Reduce data in the DATA STEP by KEEPing only the variables you do want */
data reduced_main; set main;
KEEP x y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 4. Reduce data in the DATA STEP by DROPing the variables you don’t want */
data reduced_main; set main;
DROP y;
run;

proc print data=main; run;
proc print data=reduced_main; run;

/* 5. Clean up variables using the RENAME statement within a DATA STEP */
data clean_main; set main;
rename x = ID y = month z = day;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 6. Clean up variables using a LABEL statement within a DATA STEP */
data clean_main; set clean_main;
label ID = “Identification Number” month = “Month of the Year” day = “Day of the Year”;
run;

proc contents data=main; run;
proc contents data=clean_main; run;

/* 7. FORMAT value labels using the PROC FORMAT and FORMAT statements */
PROC FORMAT;
value months 1=”January” 2=”February” 3=”March” 4=”April” 5=”May” 6=”June” 7=”July” 8=”August” 9=”September” 10=”October” 11=”November” 12=”December”;
run;

data clean_main; set clean_main;
format month months.;
run;

proc ;freq data=clean_main;
table month;
run;

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“Copyright Disclaimer Under Section 107 of the Copyright Act 1976, allowance is made for “fair use” for purposes such as criticism, comment, news reporting, teaching, scholarship, and research. Fair use is a use permitted by copyright statute that might otherwise be infringing. Non-profit, educational or personal use tips the balance in favor of fair use.”

Anayansi Gamboa has an extensive background in clinical data management as well as experience with different EDC systems including Oracle InForm, InForm Architect, Central Designer, CIS, Clintrial, Medidata Rave, Central Coding, OpenClinica Open Source and Oracle Clinical.