[NMusers] 18th Annual Scientific Meeting PAGANZ 2017 Feb 6th - 8th Adelaide Australia - Registrations and abstracts open

[David Foster]

Dear Colleagues,



We are pleased to announce the next PAGANZ scientific meeting to be held in 
Adelaide, Australia, 6th to 8th  February 2017.  Registration and call for 
abstracts is now open.



Please visit the PAGANZ home page for details and links to registration for the 
meeting: http://www.paganz.org/



Who should attend?



The PAGANZ meeting is the Australasian forum for scientists with a research and 
professional interest in the use of the population approach in pharmacokinetics 
and pharmacodynamics. A strong focus of this meeting is the application of 
population modelling and simulation techniques in the experimental, clinical 
and regulatory settings of drug development.



This meeting is designed for scientists and clinicians working in basic or 
clinical pharmacology research, pharmaceutical industry, regulatory bodies and 
postgraduate students. The meeting combines hands on workshops led by leaders 
in the field and a one and half day scientific meeting giving researchers the 
opportunity to present and discuss their own work. This is a very informal 
meeting and in the past has been very productive in helping and guiding 
researchers in this important area of pharmacology and drug development.



Population Analysis Workshops (1.5 days)



PAGANZ runs 2 workshops (Beginners & Intermediate). The workshops consist of 
lectures and hands-on exercises at a computer. During the hands-on exercises, 
tutors will be available for consultation and assistance.



The Beginners Workshop introduces the theory and application of NONMEM.



The Intermediate Workshop will cover:

1. Shiny apps in R for simulation (1/2 day)
2. Ordinary differential equations in R / PKADVAN package-analytical solutions 
in R (1/2 day)
3. Multi-state receptor theory and "biased" ligands (1/2 day).



PAGANZ Scientific Meeting (1.5 days)



The PAGANZ meeting includes a variety of free communications that highlight 
recent advances in the theory and application of the population approach to 
pharmacokinetics and pharmacodynamics in different clinical settings and in 
drug development.



Each year PAGANZ invites an international renowned speaker to give the ISOP 
lecture. This year the ISOP lecture will be given by Prof. Hartmut Derendorf, 
Department of Pharmaceutics at the University of Florida College of Pharmacy.



We look forward to seeing you next February!



Regards,



David Foster

On Behalf of the PAGANZ Organising Committee



David Foster

Senior Lecturer in Pharmacokinetics  |  P4-08  |  Playford Building
School of Pharmacy & Medical Sciences  |  Australian Centre for Pharmacometrics 
 |  Sansom Institute for Health Research

University of South Australia  |  CEA-19, GPO Box 2471  |  Adelaide, SA, 5001

P: +61 8 8302 2055  |  F: +61 8 8302 2389  |  E: 
david.fost...@unisa.edu.au

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[NMusers] $PRIOR with normal for OMEGA?

[Mark Sale]

Is it possible to use a normal prior for OMEGA? The default is inverse Wishart, 
but I'd be interested in using Normal (insuring that it is positive definite) 
Any ideas?

thanks



Mark Sale M.D.
Vice President, Modeling and Simulation
Nuventra Pharma Sciences, Inc.
2525 Meridian Parkway, Suite 280
Durham, NC 27713
Phone (919)-973-0383
ms...@nuventra.com
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of the intended recipient(s). If you have received this transmittal in error, 
please notify me immediately by reply email and destroy all copies of the 
transmittal.

Re: [NMusers] $PRIOR with normal for OMEGA?

[Gewitz, Andrew]

Mark,

Without getting too technical, recall that the inverse wishart is a 
distribution on matrices, and is naturally conjugate to the multivariate normal 
distribution (i.e. on vectors of random variables.) Generally, this is chosen 
because posterior computations are simplified and it is easy to sample from 
this posterior distribution.

What you're suggesting is the Matrix Normal distribution. Since it is not 
conjugate to the multivariate normal, posterior computations can cause 
headaches. So while it is *possible* to use such a distribution as a prior, it 
is cumbersome to work with in practice and requires thinking about some things 
like correlation and scale in non straightforward ways.





--
Andy Gewitz, PhD
Bioengineering and Therapeutic Sciences
University of California, San Francisco

On Nov 10, 2016, at 7:57 PM, Mark Sale 
> wrote:


Is it possible to use a normal prior for OMEGA? The default is inverse Wishart, 
but I'd be interested in using Normal (insuring that it is positive definite) 
Any ideas?

thanks



Mark Sale M.D.
Vice President, Modeling and Simulation
Nuventra Pharma Sciences, Inc.
2525 Meridian Parkway, Suite 280
Durham, NC 27713
Phone (919)-973-0383
ms...@nuventra.com
CONFIDENTIALITY NOTICE The information in this transmittal (including 
attachments, if any) may be privileged and confidential and is intended only 
for the recipient(s) listed above. Any review, use, disclosure, distribution or 
copying of this transmittal, in any form, is prohibited except by or on behalf 
of the intended recipient(s). If you have received this transmittal in error, 
please notify me immediately by reply email and destroy all copies of the 
transmittal.

Re: [NMusers] $PRIOR with normal for OMEGA?

[Martin Bergstrand]

Hi Mark,

Not sure about this but how about estimating THETAS scaling fix OMEGAs (e.g. CL 
= THETA(1)*EXP(THETA(2)*ETA(1)) ; $OMEGA 1 fix)? Then you can implement the 
prior in the same way for random effect parameters as you do for fixed effect 
parameters. 

Ps. Probably not be compatible with MU-parameterization. 

Best regards,
 
Martin Bergstrand, Ph.D.
Senior Consultant
Pharmetheus AB
 
+46(0)709 994 396
martin.bergstr...@pharmetheus.com
www.pharmetheus.com
 
+46(0)18 513 328
U-A Science Park, Dag Hammarskjölds v. 52b
752 37 Uppsala, Sweden

Skickat från min iPhone
11 nov. 2016 kl. 04:57 skrev Mark Sale :

> Is it possible to use a normal prior for OMEGA? The default is inverse 
> Wishart, but I'd be interested in using Normal (insuring that it is positive 
> definite) Any ideas?
> 
> thanks
> 
> 
> 
> 
> Mark Sale M.D.
> Vice President, Modeling and Simulation
> Nuventra Pharma Sciences, Inc.
> 2525 Meridian Parkway, Suite 280
> Durham, NC 27713
> Phone (919)-973-0383
> ms...@nuventra.com 
> CONFIDENTIALITY NOTICE The information in this transmittal (including 
> attachments, if any) may be privileged and confidential and is intended only 
> for the recipient(s) listed above. Any review, use, disclosure, distribution 
> or copying of this transmittal, in any form, is prohibited except by or on 
> behalf of the intended recipient(s). If you have received this transmittal in 
> error, please notify me immediately by reply email and destroy all copies of 
> the transmittal.
> 
>  

[NMusers] Mango Solutions 2017 UK & Central Europe Public training courses -Rfor PK, Clinical Trial Simulation in R & Clinical Trial Graphics in ggplot2

[Christina Halliday]

Hi Everyone,
Mango Solutions are providing the following public R training courses for 2017 
in the UK and Central Europe.
These courses have been carefully created & developed over the last decade to 
remain current and applicable specifically for the Pharmaceutical sector.

Course titles, locations and dates are as follows:

UK - Heathrow area of London

R for Pharmacometrics Wednesday 22nd and Thursday 23rd March 2017

More UK courses and dates can be found on our website at 
www.mango-solutions.com/wp/products-services/r-services/r-training/

Central Europe

R for Pharmacometrics Tuesday 27th and Wednesday 28th June 2017

Clinical Trial Simulations in R Wednesday 20th September 2017

Clinical Trial Graphics in ggplot2 Thursday 21st September 2017

Delivered by our own team of Data Scientists, all our courses are fully 
interactive (Laptops are required throughout the training), with theory backed 
up by relevant worked exercises & examples. We also provide 3 months post 
course support provided from Mango's technical helpdesk. Commercial and 
Academic rates are available for these courses.
PRIVATE COURSES are also available for these and any of our public courses 
outlines. Private training is delivered globally, catering to a suggested 
maximum of 15 attendees per course. The benefit to a private course is that it 
can be very cost effective, delivered at a time / location convenient for you 
and the course outline tailored to your specific requirements. ( Own Datasets 
can be used upon request).

For course outlines and further details on these or any of our public courses, 
or you would like to discuss a private course for your teams, please contact us 
at pha...@mango-solutions.com


Kind regards

Christina





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[NMusers] 9th PMX Network Benelux meeting - Final program

[Anne Brochot]

Dear Colleagues,

We are happy to invite you to the 9th meeting of Pharmacometrics Network 
Benelux.

Please mark the following date and time in your agenda:
When: Thursday, November 24th 2016 - 12:00 - 18:00

Where: hosted by Ablynx NV, Belgium
Technologiepark, 21
9052 Gent/Zwijnaarde

Agenda theme: "A special student session"

Registration: you can register by sending an e-mail to 
anne.broc...@ablynx.com.
There is no registration fee, but registration is required for participation. 
You can register until November 18th.

Scientific Program
12.00 - 13.00:Light lunch
13.00 - 13.15:Welcome
13.15 - 13.45:Understanding pharmacokinetics and target binding kinetics 
using the rate-limiting step approximation
  Wilbert de Witte, PhD student Leiden University 
(Netherlands)
13.45 - 14.15:Pharmacokinetics and Pharmacodynamics of hyperthermic 
intraperitoneal oxaliplatin in peritoneal carcinomatosis patients
  Carlos Pérez-Ruixo, PhD, Post-Doctoral Researcher 
 Janssen (Belgium)
14.15 - 14.45:First-pass and systemic CYP3A-mediated clearance of midazolam 
in preterm neonates
  Jantine Brussee, PhD student Leiden University 
(Netherlands)
14.45 - 15.30:Coffee/Tea Break
15.30 - 16.00:Population pharmacokinetic analysis of perioperative factor 
IX dosing in hemophilia B - Preliminary results
  Tim Preijers, PhD student University of Amsterdam 
(Netherlands)
16.00 - 16.30:In Silico Modeling Approach for the Evaluation of 
Gastrointestinal Dissolution, Supersaturation and Precipitation of 
Posaconazole: Exploring the PBPK modeling platform Simcyp®
  Bart Hens, PhD KU Leuven (Belgium)
16.30 - 17.00:Sequential PK/PD analysis of the antidiuretic desmopressin in 
children
  Robin Michelet, PhD student Ghent University 
(Belgium)
17.00 - 18.30:Closure followed by reception

We hope to see you in Gent on November 24th!

Anne Brochot
Eugène Cox
Thomas Kerbush
Charlotte van Kesteren
Ron Mathôt
Stefaan Rossenu
Laura Sargentini


On Pharmacometrics Network Benelux: the initiative aims to connect 
pharmacometricians in the Benelux (Belgium, Netherlands, and Luxembourg). The 
intention is to create an informal platform for the pharmacometrics community 
in this region of the world (in industry, academia, hospital pharmacies as well 
as regulatory bodies), with the goal to promote professional and scientific 
awareness and interactions.




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"NANOBODY" and "NANOCLONE" are registered trademarks of Ablynx N.V. 

[NMusers] SAVE the DATES!!!! NONMEM/PDxPoP workshops for 2017 presented by ICON

[Wilhelm-Lear, Lisa]

Please save the dates!  Below is a list of the workshops that we plan to 
present in 2017!

April
3-day NONMEM/PDxPoP Workshop - April 4-6, 2017 - Howard County, Maryland - 
Business Training Center

June
1-day Advanced Methods of NONMEM - June 5, 2017 - PAGE Conference - Budapest, 
Hungary

September
3-day NONMEM/PDxPoP Workshop - September 12-14, 2017 - Howard County, Maryland 
- Business Training Center

October
1-day Advanced Methods of NONMEM - October 14, 2017 - ACoP Conference - Fort 
Lauderdale, Florida

More information will be forthcoming.

Notices will be provided when registration is available for these workshops.

Looking forward to a wonderful 2017!!!

Kind regards,


Lisa R. Wilhelm-Lear
Customer Service Lead
NONMEM / PDxPoP Software

Phone:  301-944-6771
Fax: 215-789-9549
www.iconplc.com

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RE: [NMusers] Estimation method using ITS and IMP iterations

[Jonathan Moss]

Dear Nathalie

 

In answer to your question; yes, it is usual to see this "unstability" in
the final few iteration OFVs. 

When using the IMP method, I often include two sequential $EST commands. The
first command will perform optimisation of parameter estimates until a
global minimum is found. The second command will then take those parameter
estimates and calculate more precise estimates of the objective function
value. The second $EST command will have a higher ISAMPLE to reduce the
Monte Carlo noise, and have ETYPE=1 (no optimisation of parameter values). 

 

I suspect that the number of samples that you are using may not be enough,
giving large Monte Carlo noise in the OFV estimate. I suggest that you
perform another run with the parameter values set to their final estimates,
and with:

$EST METHOD=IMP ISAMPLE=1 INTERACTION LAPLACE NITER=5 SIG=3 PRINT=1
SIGL=6 EONLY=1 NOHABORT RANMETHOD=3S2

 

The higher number of samples should give a more stable result (although the
run time of each iteration will increase significantly). Taking the average
OFV of these 5 iterations will give a more accurate estimation of the final
OFV.

 

Jon

 

Jon Moss, PhD

Modeller 

BAST Inc Limited

Loughborough Innovation Centre

Charnwood Wing

Holywell Park

Ashby Road

Loughborough, LE11 3AQ, UK

Tel: +44 (0)1509 222908

 

 

 

From: owner-nmus...@globomaxnm.com [mailto:owner-nmus...@globomaxnm.com] On
Behalf Of nathalie.perda...@servier.com
Sent: 10 November 2016 07:06
To: nmusers@globomaxnm.com
Subject: [NMusers] Estimation method using ITS and IMP iterations

 

Dear NONMEM users,

 

I am building a relatively complex PKPD model (with 47 parameters and 11
differential equations).

I had problems using FOCE so I am trying this estimation method :

 

$EST METHOD=ITS INTERACTION LAPLACE NITER=200 SIG=3 PRINT=1 SIGL=6 NOHABORT
CTYPE=3 NUMERICAL SLOW

$EST METHOD=IMPMAP ISAMPLE=1000 INTERACTION LAPLACE NITER=1000 SIG=3 PRINT=1
SIGL=6 NOHABORT CTYPE=3 IACCEPT=0.4 MAPITER=0 RANMETHOD=3S2

$COV UNCONDITIONAL MATRIX=S TOL=12 SIGL=12 SLOW

 

The iteration for the ITS step seems to be quite stable with some artefacts:

iteration  175  OBJ=   4693.4674554341409

iteration  176  OBJ=   4694.2296104065535

iteration  177  OBJ=   4693.7753507970829

iteration  178  OBJ=   4693.9600270372885

iteration  179  OBJ=   4693.5732455834705

iteration  180  OBJ=   4693.6386423202493

iteration  181  OBJ=   4693.6215390721527

iteration  182  OBJ=   4693.6006496138452

iteration  183  OBJ=   4693.7877620448235

iteration  184  OBJ=   4694.1591757809929

iteration  185  OBJ=   4693.2614956897451

iteration  186  OBJ=   4693.5641640401127

iteration  187  OBJ=   4693.5575289919379

iteration  188  OBJ=   4495.6489907149398

iteration  189  OBJ=   4693.7711764252363

iteration  190  OBJ=   4693.6281175153035

iteration  191  OBJ=   4694.1171774559862

iteration  192  OBJ=   4693.7908707845536

iteration  193  OBJ=   4693.7709264605819

iteration  194  OBJ=   4495.9262902940209

iteration  195  OBJ=   4693.3321354894242

iteration  196  OBJ=   4694.3177205227348

iteration  197  OBJ=   4694.1301486616576

iteration  198  OBJ=   4694.2898587322170

iteration  199  OBJ=   4693.8304358341920

iteration  200  OBJ=   4691.6818293505230

 

#TERM:

OPTIMIZATION WAS NOT COMPLETED

 

The IMP step seems less stable :

iteration  120  OBJ=   4314.8310660241377 eff.= 446. Smpl.=
1000. Fit.= 0.96389

iteration  121  OBJ=   4326.9079856676717 eff.= 448. Smpl.=
1000. Fit.= 0.96409

iteration  122  OBJ=   4164.6649529423103 eff.= 479. Smpl.=
1000. Fit.= 0.96392

iteration  123  OBJ=   4299.9887619753636 eff.= 432. Smpl.=
1000. Fit.= 0.96395

iteration  124  OBJ=   4303.9571213327054 eff.= 399. Smpl.=
1000. Fit.= 0.96349

iteration  125  OBJ=   4328.9835950930074 eff.= 417. Smpl.=
1000. Fit.= 0.96423

iteration  126  OBJ=   4304.3861595488252 eff.= 550. Smpl.=
1000. Fit.= 0.96392

iteration  127  OBJ=   4291.0862736663648 eff.= 422. Smpl.=
1000. Fit.= 0.96430

iteration  128  OBJ=   4326.2378678645500 eff.= 407. Smpl.=
1000. Fit.= 0.96409

iteration  129  OBJ=   4157.5352046539456 eff.= 406. Smpl.=
1000. Fit.= 0.96404

iteration  130  OBJ=   4332.6894073732456 eff.= 399. Smpl.=
1000. Fit.= 0.96399

iteration  131  OBJ=   4357.5343346793761 eff.= 493. Smpl.=
1000. Fit.= 0.96414

Convergence achieved

iteration  131  OBJ=   4336.1893012015007 eff.= 417. Smpl.=
1000. Fit.= 0.96369

 

#TERM:

OPTIMIZATION WAS COMPLETED

 

I think the ITS step is OK with an objective function ~ 4690.

The "unstability" of the IMP step  is it usual ? Nonmem is completed at the
end..

 

I want to trust in this