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Full summary molecular therapy
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Full summary for the course molecular therapy (NWI-BM078) given in 2023 - Radboud University, Nijmegen
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Molecular Therapy: Lecture 1Personalized healthcare – 04-09-2023
Exponential Developments in Omics
Technologies
Nowadays, genomics is able to test 3 billion DNA bases in 1
assay. Proteomics, glycomics and metabolomics are able to test
10.000 – 50.000 proteins and metabolites in 1 assay. The
analysis of this data is often able to give an overview of the
issue.
Molecular biomarker data (omics) plays a key role in
personalized healthcare as it allows for a personalized diagnosis,
therapy and participation, all leading to personalized healthcare. It also allows for therapy
monitoring during the process.
Personalized Healthcare in Rare Metabolic Diseases
In the case we have seen during the lecture, the diagnosis happened via genetic and metabolic
screening.
During the genetic screening, a trio whole exome sequencing was performed – meaning the exomes
of both parents and the child were sequenced –, but no distinct genetic cause was found. They did
discover a heterozygous mutation in SEC23B, which is associated with dyserythropoietic anaemia
type II (CSAII) in the case of a bi-allelic mutation, so it was the cause either.
The metabolic screening involved a urine organic acids and
purine/pyrimidine analysis. The results show very high orotic
acid levels (3404 μmol/mmol kreat; reference 0-4), without
indication in serum amino acids for a urea cycle disorder. This
lead to the diagnosis of uridine monophosphate synthase
(UMPS) deficiency / hereditary orotic aciduria. The
personalized diagnosis in this case is high orotic acid and the
personalized therapy was uridine supplementation.
The treatment of UMPS deficiency had already been described back in 1969 by Becroft et al., during
which they used a long-term therapy with uridine and a trial of uracil. In 2014, Balasubramaniam et
al. published a paper on the inborn errors of pyrimidine metabolism, clinical update and therapy.
There have been 15 cases reported in the past and 3 newly diagnosed patients have appeared in the
Netherlands since. The treatment for this disease is uridine tri acetate (a stable form of uridine),
which is registered in the USA. It costs 800.000 euro per patient, per year and is not registered in the
Netherlands, meaning that it would be difficult to obtain.
An alternative solution to the uridine tri acetate is a food supplement from the local drugstore (e.g.
kruidvat), which was originally created to support concentration. It comes at a price of 36.95 euros
per 50 gram. In January 2020, the child started on 60 mg/kg. The effects were that no blood
transfusions were needed anymore, the reticulocytes and leucocytes increased. The child had more
energy, his appetite and growth improved as well. The orotic levels are still high and the effects on
the kidney can be seen. A relatively high dose of uridine can also be seen.
,The lessons that this case taught us on personalized diagnosis were that the technology innovation is
a driving impact on personalized healthcare and that the combination of genetic and metabolic
screening is a strong approach towards identifying the mechanism of disease. What we learned on
personalized medicine were the impressive effects of uridine therapy, the increase in quality of life
for both the patient and family and that there is an issue regarding expensive medication versus
cheap supplements.
Personalized Healthcare in Multiple Myeloma
MM is the 2nd most common
haematological malignancy. In this disease,
monoclonal plasma cells can be found in
the bone marrow, these monoclonal
plasma cells secrete a monoclonal (M)
protein. It is treated by heavy
chemotherapy, steroids and specific drugs.
However, about 50% of the patients
achieve a state called minimal residual
disease (MRD), in which a relapse is more
likely to occur. Therapy should be restarted
as soon as the disease relapses.
Because of this, a sensitive and easy detection of MRD is needed. The diagnostic test for MM is a
blood M-protein test by gel electrophoresis. The current tests for MRD are to isolate the bone
marrow for stromal cells and analysis of those stromal cells by flow cytometry or genomics (PCR,
NGS). This is a cumbersome and invasive procedure for the repetitive monitoring of MRD and a
sampling error can be caused by tumour heterogeneity.
MS MRD Method
A different approach would be the direct measurement of the
rearranged region of the M-protein by targeted proteomics in
plasma samples (= MS MRD method).
Mass spectrometry strongly increases the sensitivity in the
detection of M-protein. They punch out the M protein bands
from archived gels and perform a mass spec analysis via either
de novo sequencing or semi-quantitation. This allows the
monitoring of patients over time. This leads to the conclusion
that the MS MRD method is feasible for monitoring and early detection of relapses and that re-
analysis of archived gels is possible.
From research, we can conclude that sequencing-MRD and
MS-MRD have a similar sensitivity and perform equally well as
a prognostic biomarker.
Ongoing diagnostic assay development includes the analytical
validation, setting an internal standard and QCs, less parallel
reaction monitoring (PRM) and more data-independent
acquisition (DIA). They are also currently working on improving peptide prediction and real-time
identification.
,The lessons learned from this disease are that technology innovation truly is a driving impact in
personalized healthcare, as mentioned above. On top of this, we can see that the analysis of dynamic
biomarkers is key in monitoring MRD and that MS has an added value and good potential here. The
collaboration between clinic, lab specialists, proteomics labs, academics and the industry is needed
when it comes to analytical development and clinical validation.
Clinical Omics Data to Drive Personalized Healthcare
Personalized analysis happens through genomics, epigenetics, transcriptomics, proteomics, … The
bottle neck is the diagnosis of new disease mechanisms through the obtained data as efficient
analysis methods need to be used. These methods include deep learning and AI integrated
diagnostics. The analysis of the data can be used for the initiation and monitoring of personalized
therapies.
Digital Biomarkers
We live in a digital world, meaning that digital biomarkers are used as well. The advantages
compared to the traditional biomarkers can be found in the table below.
Digital Biomarkers Traditional Biomarkers
Continuous monitoring 1 snapshot observation
Real world data Data from a clinically controlled circumstance
More comprehensive and rich data sets
Truly personalized
Strong potential in molecular, clinical, digital
and environmental biomarkers for optimal
insight in complex biological systems
Better basis to drive personalized healthcare
Better support for phase 1-4 in clinical trials
Personalized Parkinson Project
The PPP examines disease progression through broad biomarkers in early Parkinson’s disease. It
involves blood sampling, lumbar puncture, stool sampling, advanced neuroimaging, ECG and Holter,
clinimetrics and self-assessments and a verily study watch. The verily study watch has schedule tasks,
such as seated rest and hand opening. The average wear time indicates that patients are compliant
with the use of the watch. The virtual exam enables frequent and reliable remote measurements of
motor function.
Personalized Health(care) Model
Personalized healthcare can be divided
into primary prevention (preventing
getting sick), secondary prevention
(preventing the increased risk) and
tertiary prevention (preventing severe
complications).
Personalized data-driven healthcare
should monitor your health in the
background and only alert you when
you are at risk. It should advice the
patient what to do (e.g. phone app that detects heart attacks based on speech).
, Innovational Gaps
There are 3 innovational gaps: research to research (lack of
reproducibility), research to clinic and research to society.
There’s a gap in biomarker innovation, as the number of
biomarkers discovered are about 5/working day. Clinical
validation/confirmation only confirm/validate 1 biomarker per
1-3 years and diagnostic tests only validate 1 biomarker per 2-
10 years.
There are also quality issues in commercial health biomarker analyses, as they are not always
accurate. There are big hopes for AI in processing of big data, however, bad data will remain bad data
even if made FAIR (findable, accessible, interoperable and reusable) and AI-ready.
There is a big “valley” between the discovery and the use of the discovery in clinical settings. This
indicates that there is a lot more research needed to confirm the discovery and implement it in a way
it is applicable in a clinical setting and we thus need to join forces in quality (actually bringing the
discovery to the clinic) and not quantity (bringing out more discoveries). While doing this, we need
to focus on the end user: the patient/citizen and the ethical, legal and societal aspects regarding the
discovery.
Afterthought
There are multiple sources of data that can be used for personalized healthcare, however, they each
show different data, leading to different images of the patient. Only together, they can give a proper
image of the health of the patient.
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