Clinical Pharmacology

Its been a while since I had posted anything here. While I have been busy juggling between being a dad to a month old boy and trying to finish thesis work, I try to find time to recap and update myself on PK/PD. Here is a link with lots of presentations on various concepts of clinical pharmacology by authors of the book "Principles of Clinical Pharmacology".

http://www.cc.nih.gov/training/training/principles/schedule.html

Have fun learning!

PKPD Regulatory Guidances

After a short hiatus, I am back and hope to continue posting frequently. Ok. If you are planning to work in the industry or already dealing with population PK studies, then you may want to read the guidances pertaining to PK/PD. These guidances provide recommendations on study design, data analysis and report writing which can be very helpful in your work.

Listed below are some guidances where PopPK or PK/PD studies are recommended by FDA or EMEA.

Population Pharmacokinetics [PDF] - This document provides recommendation on the study design and execution, model development & validation and reporting the results of a PopPK analysis. Some examples of how the results are used in drug labeling are also provided. However, the document doesnt delve deep into the qualification of softwares used for such analysis. Neither does it talk about any preferred softwares like NONMEM or SAS or Splus.

Guideline on Reporting the Results of Population Pharmacokinetic Analysis [PDF] - This EMEA guideline provides in detail the information required in a report submitted for their review. I like this document over FDA's guidance especially because they spell out exactly the kind of information one needs to report in terms of NONMEM lingo eg, choice of analysis (parametric or non parametric or bayesian), estimation methods (FO vs FOCE vs FOCE INTER), GOF plots like DV vs PRED, DV vs IPRED (with line of identity and trend line (:>)) and so forth.

Other guidances that recommend PK/PD evaluations include

• FDA Final Guidance on exposure-response relationships - study design, data analysis and regulatory applications. [PDF]
• Pharmacokinetics in Pregnancy— Study Design, Data Analysis, and Impact on Dosing and Labeling [PDF] -
• Guideline on the Role of Pharmacokinetics in the Development of Medicinal Products in the Paediatric Population [PDF] - Efficay and safety data from adults can be extrapolated to obtain PK information for paediatric patients. When PK studies are to be carried out in paediatric population, PopPK studies with sparse sampling methodolgy seems to be the best approach. In young patients, there is rapid maturation of organs that are involved in drug absorption, distribution and elimination necessiating different dosage regimen. Thus for drugs that are intended to be used in paediatrics, this document provides recommendations on the study design of PK studies.

More in my next post...

AAPS Annual Meeting

I will be in Atlanta for AAPS meeting between 15-20 Nov. I would be happy to meet you, if you are planning to attend. Let me know via comments or by emailing me at ganesh.mugundu [at]gmail.com.

NONMEM Resources

I started learning NONMEM through a local discussion group headed by Dr. Alexander A. Vinks, at Cincinnati Childrens hospital. The small group comprised of few MS/PhD graduate students (including me) and faculty from pharmacy/mathematics. We have been meeting biweekly since 2006 and discuss about modeling data using NONMEM and other statistical softwares. Each of us had an opportunity to use real clinical data obtained from paediatric patients and develop PK/PD models to describe the data. We had guest lectures by Nick Holford MD, Dr. Roger Jeliffe MD, Jurgen Bulitta PhD about the use of NONMEM, USC Pack in population PK/PD modeling. Modeling and simulation has now become an integral part of drug development process and is being used regularly to model preclinical/clinical data.

Ok. My main objective of this post was to share some resources that are available to learn model data using NONMEM. In addition to the above discussion group, I have learnt immensely from the following resources.

a. Resources by Nick Holford: These course materials are freely available with enough background information to start performing population PKPD analysis.

1. Pharmacometrics
2. Advanced Pharmacometrics

b.Resource Facility for Population Pharmacokinetics[RFPK] – When accessing this site, you will have to fill a short questionnaire before downloading or viewing the tutorials/ presentations.

1. Tutorials
2. Presentations
3. Datasets

c. Nonlinear mixed effect models: an overview and update [PDF]

d. Laboratory of Applied Pharmacokinetics (LAPK) teaching resources by Roger Jeliffe.

1. New Advances in PK/PD Modeling [PDF]
2. Pop PK: Parametric & Non parametric Approaches [PDF]
3. Bayes Theorem and Other PK resources [PDF]

e. Pharmacometrics by ACCP

f. Regulatory Guidances

1. US FDA Population Pharmacokinetics [PDF]
2. EMEA Guideline on Reporting the Results of Population Pharmacokinetic Analyses [PDF]

If you know of more resources that can be added to this list, please feel free to provide the information by way of comments. Good night!

Data Presentation

Here is an excellent video on data presentation by Hans Rosling.

Let me know your thoughts if you enjoyed this video!

Good Modeling

Here is something to think about...

From a mathematical point of view, the art of good modeling relies on: (i) a sound understanding and appreciation of the biological problem; (ii) a realistic mathematical representation of the important biological phenomena; (iii) finding useful solutions, preferably quantitative; and most crucially important, (iv) a biological interpretation of the mathematical results in terms of insights and predictions. The mathematics is dictated by the biology and not vice versa. Sometimes the mathematics can be very simple. Useful mathematical biology research is not judged by mathematical standards but by different and no less demanding ones.

- Jim Murray, 1993

Hepatotoxicity in Drug Development

Liver toxicity has been one of the most common reasons for drug withdrawal in the past e.g Benoxaprofen (Oraflex), ticrynafen (Selacryn), bromfenac (Duract) and troglitizone (Rezulin). It has lead to black box warning for drugs like rifampicin, acetaminophen, valproic acid etc, due to their potential to cause liver injury. In some cases, drugs have not been approved because of its hepatotoxicity. Acetaminophen is a classic example where drug induced liver injury (DILI) is observed due to overdosage. The mechanism involves formation of a highly reactive metabolite N-Acetyl-p-benzoquinoneimine, by cytochrome P450 enzymes (CYPs) CYP2E1, CYP1A2, and CYP3A4. Accumulation of this metabolite results in cell death and hepatocellular necrosis. Patients with acetaminophen hepatotoxicity are treated with N-acetylcysteine, but still approximately 500 patients die each year.

In the course of drug discovery and development, why is it difficult to determine the potential of a new chemical entity to cause hepatotoxicity or DILI? Though every compound undergoes rigorous testing in animal models and in vitro hepatocytes, most of the compounds pass through without being identified as hepatotoxic, probably due to lack of an appropriate model or due to differences in absorption, distribution, metabolism, elimination (ADME) between humans & animals. Other reasons include age, sex, genetic polymorphisms, disease conditions, drug -food and drug-drug interactions (CYP Induction/Inhibition). All these factors decrease the predictive power of non-clinical studies.

An increase in levels of the liver enzymes like alanine aminotransferase (ALT) and aspartate aminotransferase (AST), alkaline phosphatase (ALP) and total bilirubin are signs of liver toxicity. Evaluation of these enzymes are routinely performed in clinical trials to assess drug induced liver injury. According to FDA guidance document, Hy’s Law (Hy Zimmerman) in determining DILI cases have the following three components:

“1. The drug causes hepatocellular injury, generally shown by more frequent 3-fold or greater elevations above the ULN of ALT or AST than the (nonhepatotoxic) control agent or placebo.
2. Among subjects showing such aminotransferase (AT) elevations, often with ATs much greater than 3xULN, some subjects also show elevation of serum TBL to >2xULN, without initial findings of cholestasis (serum alkaline phosphatase (ALP) activity >2xULN).
3. No other reason can be found to explain the combination of increased AT and TBL, such as viral hepatitis A, B, or C, preexisting or acute liver disease, or another drug capable of causing the observed injury.”

If a patient has found to have AT 3xULN or TBL is greater than 2xULN, the tests need to be repeated within 48-72 hours to confirm the abnormalities.

A recent example of hepatotoxicity observed in a new drug and the response of FDA has been cited in a white paper formulated for the workshop “Assessing and Accelerating Development of Biomarkers for Drug Safety” as follows

“A major pharmaceutical company submitted an NDA application for treatment of a chronic disease. The FDA agreed with the sponsor’s efficacy data. However, it was noted that among ~4,000 treated patients in clinical trials, two developed elevations in both serum alanine aminotransferase and bilirubin. As a prerequisite for approval, the company was told to conduct a new safety study of 10,000 patients treated with drug for one year, and to also include an additional 10,000 subjects receiving comparator treatment for one year. This news will cost the company >$200M to conduct the trial, ~3 years off patent, and loss of market entry position in class.”

This clearly shows how important it is to determine the toxicity to liver during the phases of drug development. I guess this post will stimulate some fruitful discussions and ideas related to DILI.

Other Guidances/References: EMEA -Non-Clinical Guideline On Drug-Induced Hepatotoxicity[PDF], FDA White Paper on Nonclinical Assessment of Potential Hepatotoxicity in Man [PDF]

Please feel free to share this post on technocrati , del.ici.ous and digg.

A=X+Y+Z: Sucess Formula

Good Morning!

Here is something simple to think about-

"If A equal success, then the formula is A equals X plus Y and Z, with X being work, Y play, and Z keeping your mouth shut - Albert Einstein

Wish you "A" in your day!

MWSUG - Midwest SAS User Group Scholar

I was fortunate enough to be selected as a student scholar in the recent midwest SAS user group conference held at Mariott Hotel, Indianapolis. This scholarship included a free registration and accomodation during the conference. It was a unique opportunity to mingle with SAS/JMP users from industry & academia and lean from the experts. The presentations were in the following categories

Application Development
Data Visualization
Pharmaceutical Applications
SAS Presents
Statistics and Data Analysis
Tutorials
JMP

Being a midwest user's conference, I think MWSUG 2008 had around 400-500 attendees and was a small one when compared to AAPS with approximately 10,000 attendees. Dr. John Sall, Co-founder and Executive Vice President of SAS Institute and leader of the JMP business division was the key note speaker. He talked about the journey of JMP since late 1980s to the current version of JMP7.0. I especially enjoyed the talk on Mixed Effect Models and Bayesian analysis using SAS. I am not a SAS/JMP expert, but am comfortable using them for some of the statistical analysis.

In addition to the various presentations and hands on SAS workshop, MWSUG included a Las Vegas Casino Night with games like Craps, Blackjack, Roulette, 3-Card Poker, Money Wheel and Texas Hold’em Poker area. Each of us received a $500 fake money to play the games. This was the second time I was ever gambling in my life (can you believe that?) and in a Mariott turned Las Vegas Casino. The first time was when I visited Las Vegas and was trying my hand at the slot machines. Anyways, I did have fun on the casino night.

Did you have an experience similar to this? I would be happy to hear and learn from you!

Population Pharmacokinetics/Pharmacodynamics

Population pharmacokinetics (PopPk) is the study of variability in plasma concentrations between and within individuals. It helps in identifying the demographic, pathophysiological, environmental and drug related factors that contribute to the variability observed in safety and efficacy of a drug. Unlike traditional PK studies where healthy volunteers are subjected to intensive sampling to determine PK parameters, PopPk studies offer an advantage of estimating PK parameters in target patient population with sparse sampling methodology. PopPK uses a nonlinear mixed effects modeling (NONMEM) approach. Variability in NONMEM is characterized in terms of fixed and random effects. The fixed effects are population average values of pharmacokinetic parameters such as clearance & volume of distribution, that may in turn be a function of patient characteristics. The random effects quantify the variability that is not explained by the fixed effects. These random effects include inter-subject, intra-subject, inter-occasion and residual variability. This methodology has also been extended to model population pharmacokinetic/pharmacodynamics (Pop PK/PD) relationships. This NONMEM approach was introduced by Lewis Sheiner and Stuart Beal approximately 30 years back. It has now become an integral part of drug development in industries and FDA review process.

Some of the softwares that have been used for modeling are as follows
NONMEM (most widely used)
ADAPT
Monolix
NPEM
NPAG (USC*PACK)
WinNonmix
Kinetica
SAS (PROC NLMIXED)
Splus

Pop PK/PD has led to a new discipline known as pharmacometrics. Barrett et al in J Clin Pharmacol 2008;48:632-649 define “Pharmacometrics” as that branch of science concerned with mathematical models of biology, pharmacology, disease, and physiology used to describe and quantify interactions between xenobiotics and patients, including beneficial effects and side effects resultant from such interfaces. So if you are a pharmacist, pharmacologist, statistician or biomedical engineer with good understanding of pharmacokinetics, pharmacodynamics, biological sciences, statistical and computational tools, you have a good chance to become a pharmacometrician. They are paid well both in the industry and the FDA. Infact, FDA has its own pharmacometrics group headed by Joga Gobburu and are promoting the use of modeling & simulation in drug development.

Are you a pharmacometrician or a PK/PD scientist? Is there a specific software that you use to model the data?