Modelling & Simulation at ASCPT2019 – a short impression

The annual meetings of the American Society for Clinical Pharmacology & Therapeutics have been for me the most inspiring conferences that I visited. This year’s meeting did not disappoint either, here is why.

I feel that the PAGE meeting in Europe discusses modelling and simulation from a more technical perspective, whereas the perspective of the ASCPT annual meeting is more related to clinical pharmacology. Therefore, I can recommend to every modeller to visit this meeting at least once. Don’t worry about not finding anything that you like, because this conference has different parallel sessions and one could spend the full 3 or 4 days talking and listening about modelling

The 2019 session of the ASCPT annual meeting started with 2 pre-conferences; one on ‘PBPK modelling for the development and approval of locally acting drug products’ and another on ‘advancing QSP toward predictive drug development: from targets to treatments’, of which I attended the latter. The topics covered by these pre-conferences already show the prominent role that modelling has within this annual meeting.

At the QSP meeting, it was inspiring to see what a dynamic field of research we’re in. QSP was in its early stage when I was introduced to modelling in 2013/2014, and has quickly evolved and been applied to different biological systems to help us increase the understanding about the effect of pharmacological intervention. It was also good to see that the regulatory perspective on QSP tends more towards willingness versus resistance.

At this meeting, I was also introduced to the Gartner hype-hope cycle for advancing technologies, depicted below.

Wikipedia image on the Gartner hope-hype cycle

This is a nice graph to identify where we currently are on this curve, thinking about pharmacometrics, QSP or machine learning. This figure shows the initial excitement when a new technology is launched, with the quick decrease in expectations when reality kicks in (the trough of disillusionment) and the rebound that causes a technology to actually be used in the long run and achieve a plateau of productivity. One could ask her/himself where pharmacometrics and QSP currently lies on this curve, did we pass the trough of disillusionment and are we currently going towards the implementation without the inflated expectations?

Side note: when I wrote this post, I noticed many similarities with my PhD that I am currently trying to finish. I started full of energy in my first year by taking on many projects, setting ambitious goals and aiming to solve all the problems in growth hormone research. Then, reality kicks in and the discrepancy in the reporting of outcomes in acromegaly and no methodology present to quantify pulsatile profiles in non-linear mixed effects models caused me to reach my trough of disillusionment. Then, slowly but steadily, new projects were started that will, hopefully, result in my thesis to be completed in the next couple of months.

The official conference started on Thursday with modelling sessions ranging from Physiologically Based Pharmacokinetic (PBPK) Modeling in Vulnerable/Special Populations, to the Sheiner-Beal Pharmacometrics Award lecture and a point/counterpoint debate with the top scientists in our field.

Additionally, at this year’s meeting, different sessions on data science, machine learning, deep learning were present, all showing the usability of these techniques in clinical pharmacology. For example, did you know that we can apply machine learning to inform clinical trial dosing regimens? (presented during the Innovation Forum, see Pratik Shah, pdf)

However, I personally would look forward to see a comeback of the Pharmacometrics Skills Competition to highlight the need for communication in pharmacometrics (perhaps next year?).

Sheiner-Beal Pharmacometrics Award Lecture – Pharmacometrics 3.0

This year, Joga Gobburu received the Sheiner-Beal award for his excellent work in pharmacometrics and provided a promising perspective for the field of pharmacometrics, labelled Pharmacometrics 3.0. I can only agree with the direction that he proposed; to see and to use pharmacometrics as a decision support system. Either using this in industry, to make decisions on new compounds but also, and perhaps more importantly, as a bridge with clinicians that can improve (personalized) patient care.

He also highlighted the need for new software packages that incorporate all different tools that we used. This was followed by by the announcement of PuMaS software, an open source package that will be released around April 15th 2019. See the figure below for the promised functionality.

Functionality of PuMaS. Copied from

MID3: Mission Impossible or Model-Informed, Drug Discovery and Development?

If there is one session that you should watch on ASCPT Replay it is the MID3:Point-Counterpoint session. It discussed many points that probably all of us have encountered, such as: did pharmacometrics live up to its expectations? Should pharmacometricians be part of a project team? Are all models wrong but can they also be useful (or should we say, are all models hypothesis)? Can ‘wrong models’ also be useful? Do we need complicated models or is a simple model fit for purpose? and what is the ideal background for an MID3 scientist of the future, a biologist or a statistician?

Especially for me as a junior pharmacometrician, it is interesting to see the leaders in our field (think of Piet Hein van der Graaf, Stacey Tannenbaum, Joga Gobburu, Daren Austin, Daniele Ouellet, Marc Gastonguay, Oscar Della Pasqua) discuss these points from both sides of the spectrum, arming us with the information, examples, and metaphors we may need during these inevitable discussions…

In conclusion, I think that the ASCPT annual meeting is worth the trip, even from Europe, for someone working or interested in the field of pharmacometrics and I am thankful to the Centre for Human Drug Research, Leiden University and an ASCPT travel grant that I had the possibility to visit this meeting for the past 3 years!

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