AG&M annual report 2018

Robert de Jonge and Ittai Muller
AG&M Science Impressions 2018
Prof. dr. Robert de Jonge (Clinical Chemistry), dr. Gerrit
Jansen (Rheumatology) and Prof. dr. Jacqueline Cloos
(Hematology).
The main goal of this research is to set up and
implement MTX therapeutic drug monitoring (MTX-
TDM) combining analytical techniques (such as LC-MS/
MS), molecular techniques (such as RNA-sequencing)
and computational techniques (such as machine
learning) to potentially aid clinicians in optimizing MTX
dosing for personalize therapy. Recently, a novel and
sensitive LC-MS/MS-based method was developed
for measuring the enzymatic activity of FPGS in small
numbers of patient cells. In addition, beyond traditional
measurements of MTX-PGn in erythrocytes, improved
sensitive LC-MS/MS techniques now allow MTX-PGn
analysis in peripheral blood mononuclear cells, which
as immune-related cells are more relevant from a
disease pathophysiology perspective. Recently, Ittai’s
research revealed that alternative pre-mRNA splicing
of metabolic enzymes in patients could have predictive
value for drug response. Specifically, his research
showed a significant association between whole blood
expression of a partial intron 8 retention (FPGS 8PR)
of FPGS pre-mRNA and disease activity score (DAS)
in MTX-treated rheumatoid arthritis (RA) patients.
These studies will now be extended to global premRNA
splicing patterns as predictive markers for drug
response using next-generation sequencing techniques.
Altogether, the above-mentioned information can be
implemented in novel drug response prediction models
using machine learning algorithms. By combining novel
data such as alternative pre-mRNA splicing events,
MTX-PGn levels, and FPGS activity, together with
clinical parameters, large databases can be analysed
and used for predictive modeling of MTX-response
through machine learning. In cooperation with the
Computational Intelligence group of the VU University
Amsterdam (Dr. M. Hoogendoorn), Helen Gosselt (PhD
Candidate Erasmus MC) implements machine learning
to improve models for RA MTX response prediction.
These prediction models can then be used to aid
rheumatologists in choosing the optimal treatment
strategy but also help adjust individual patient MTX
doses to compensate for inter-patient variability
possibly preventing toxicity and improving efficacy.
While thus far most of the MTX-related TDM research
has been performed on RA, in collaboration with Reade
(Prof. Dr. M.T. Nurmohamed, Drs. R.C.F. Hebing), the
research field is expanding to other disease areas in
which MTX is the main therapeutic component. To this
end, a multicenter, prospective study involving the
UMCs and several top-clinical and peripheral hospitals
has been initiated to study MTX-TDM in Crohn’s
Disease (Amsterdam UMC: Prof.Dr. G. Bouma, Dr. N.
de Boer, Prof. Dr R. Mathôt; UMCU: Dr. H. Fidder, Dr. B.
Oldenburg, Dr. M. Bulatović-Ćalacan; PhD student: M.
van de Meeberg) to build prediction models.
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