PSY410 NIU Animal Model Research in Psychiatry Research In a one page single-spaced essay using 3-5 references: “Discuss the future of animal model researc

PSY410 NIU Animal Model Research in Psychiatry Research In a one page single-spaced essay using 3-5 references: “Discuss the future of animal model research in Psychiatry”. Please use the writing example I attached as a template. Emphasize the significance and limitations of the research approach. In the field of medicine, precise medication has revolutionized the diagnosis and
treatment of most diseases. However, it seems as though psychiatry is lagging behind. Due to the
stigma that these brain disorders are often coined “mental illnesses”, they are still diagnosed
commonly “on the basis of symptoms” (4). Dr.Insel, an American psychiatrist, has stated that
behavior is the last thing to change when dealing with these brain disorders (1). Thus, the future
of assessment for brain disorders is focused on molecular psychology, that involves connecting
normative modelling with quantitative neuroimaging biomarkers to help assist with diagnosis
and treatment. Dr. Amen, another American psychiatrist, has criticized that “psychiatrists are the
only specialists that rarely look at the organ they treat” (2). The field of psychiatry must start
improving upon previous diagnosis methods in order to keep up with the revolutionizing of
modern medicine.
A clear problem with the previous method of diagnosing mental illness is that it relied
heavily on the symptoms of the patient. If the patient exhibited symptoms that checked off the
boxes of a disease, all the psychiatrist had to do was connect the dots. One of the biggest
problems with this is “the heterogeneous biology underlying psychiatric disorders at the level of
the individual participant” (4). A group of individuals could be given the same diagnosis, but be
vastly different in their underlying biology and the way they present clinically. As the field of
psychiatry is advancing, one of the goals is to step away from these heterogeneous grouping, and
instead “understand healthy variation and move beyond simple dimensional theories of mental
disorders” (4).
One way to do this is through creating clusters. According to Dr.Insel, one would start
with grouping individuals based on symptoms. From here, the heterogeneous group would be
separated into clusters using integrated data such as genetics, physiology, and brain activity.
Having these similar clusters allows better prediction of treatment responses (3). Yet, a downside
to this is that “clustering assumes that the clinical group can be cleanly partitioned into
subtypes”. Instead of focusing on individual variation, clustering focuses of “group averages”
(4).
Another innovation that will affect the future of the assessment of brain disorders is
normative modelling. This method provides “a way to quantify and characterize the manner in
which different individuals deviate from the expected pattern and from one another” (4). This
can be connected to the idea of epigenetics, where certain genes can be turned on and off through
processes such as DNA methylation. Disorders such as schizophrenia are not controlled by a
single gene, rather it is affected by multiple. Thus, normative modeling “accommodates the
convergence of multiple pathological pathways on the same symptoms” for different people (4).
Normative modelling is also very flexible, in that it can deal with different phenotypes using
both clinical and biological measures. Normative modelling poses an advantage over clustering
because it deviates away from averaging the group, and starts to understand variation in the
individual while mapping deviations.
Unfortunately, there are a few limitations to normative modelling. For one, it does not
directly show whether the deviations are clinically relevant. It could be possible that the
deviations are simply caused by site variation, or too small of a population data was taken.
However, if the data is taken with careful consideration into factors such as genotype and
environment, then normative modeling is an excellent future approach for the field of psychiatry.
By being able to see quantitatively how individual variability “turns into vulnerability or
resilience”, psychiatrists can modify treatment options to the individual (4). Therefore, the future
of molecular psychology rests in techniques, such as clusters and normative modelling, that can
bring precision medicine into the field of psychiatry.
Citations
1. Insel, Thomas. “Toward a new understanding of mental illness.” TED, Jan 2013,
https://www.ted.com/talks/thomas_insel_toward_a_new_understanding_of_mental_il
lness?language=en.
2. Amen, Daniel. “The most important lesson from 83,000 brain scans.” TED, Oct 2013,
https://www.youtube.com/watch?v=esPRsT-lmw8.
3. Insel, Thomas R., and Bruce N. Cuthbert. “Brain Disorders? Precisely.” Science,
American Association for the Advancement of Science, 1 May 2015,
science.sciencemag.org/content/348/6234/499.
4. Marquand, Andre F., et al. “Conceptualizing Mental Disorders as Deviations from
Normative Functioning.” Nature News, Nature Publishing Group, 14 June 2019,
www.nature.com/articles/s41380-019-0441-1.
MINI REVIEW
published: 22 October 2018
doi: 10.3389/fpsyt.2018.00512
Understanding and Predicting
Antidepressant Response: Using
Animal Models to Move Toward
Precision Psychiatry
David P. Herzog 1,2 , Holger Beckmann 2,3 , Klaus Lieb 1,2 , Soojin Ryu 2,3 and
Marianne B. Müller 1,2*
1
Department of Psychiatry and Psychotherapy, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany,
Focus Program Translational Neurosciences, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany,
3
German Resilience Center, Johannes Gutenberg University Medical Center Mainz, Mainz, Germany
2
Edited by:
Frank M. Schmidt,
Universitätsklinikum Leipzig, Germany
Reviewed by:
Chiara Fabbri,
King’s College London,
United Kingdom
Casimiro Cabrera Abreu,
Queens University, Canada
*Correspondence:
Marianne B. Müller
marianne.mueller@uni-mainz.de
Specialty section:
This article was submitted to
Mood and Anxiety Disorders,
a section of the journal
Frontiers in Psychiatry
Received: 30 July 2018
Accepted: 28 September 2018
Published: 22 October 2018
Citation:
Herzog DP, Beckmann H, Lieb K,
Ryu S and Müller MB (2018)
Understanding and Predicting
Antidepressant Response: Using
Animal Models to Move Toward
Precision Psychiatry.
Front. Psychiatry 9:512.
doi: 10.3389/fpsyt.2018.00512
Frontiers in Psychiatry | www.frontiersin.org
There are two important gaps of knowledge in depression treatment, namely the
lack of biomarkers predicting response to antidepressants and the limited knowledge
of the molecular mechanisms underlying clinical improvement. However, individually
tailored treatment strategies and individualized prescription are greatly needed given
the huge socio-economic burden of depression, the latency until clinical improvement
can be observed and the response variability to a particular compound. Still, individual
patient-level antidepressant treatment outcomes are highly unpredictable. In contrast
to other therapeutic areas and despite tremendous efforts during the past years, the
genomics era so far has failed to provide biological or genetic predictors of clinical utility
for routine use in depression treatment. Specifically, we suggest to (1) shift the focus
from the group patterns to individual outcomes, (2) use dimensional classifications such
as Research Domain Criteria, and (3) envision better planning and improved connections
between pre-clinical and clinical studies within translational research units. In contrast
to studies in patients, animal models enable both searches for peripheral biosignatures
predicting treatment response and in depth-analyses of the neurobiological pathways
shaping individual antidepressant response in the brain. While there is a considerable
number of animal models available aiming at mimicking disease-like conditions such
as those seen in depressive disorder, only a limited number of preclinical or truly
translational investigations is dedicated to the issue of heterogeneity seen in response
to antidepressant treatment. In this mini-review, we provide an overview on the current
state of knowledge and propose a framework for successful translational studies into
antidepressant treatment response.
Keywords: animal model, antidepressant, depression, non-response, response, response prediction, translational
medicine
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October 2018 | Volume 9 | Article 512
Herzog et al.
Understanding and Predicting Antidepressant Response
INTRODUCTION
CURRENT CHALLENGES IN
ANTIDEPRESSANT DRUG TREATMENT:
TO RESPOND OR NOT TO RESPOND,
THAT IS THE QUESTION
Major depressive disorder (MDD) is the second leading
cause of disease burden worldwide, thus constituting
serious socio-economic threat for modern societies (1, 2).
Combined epidemiological and economic data on depression
in Europe revealed that it is the most costly brain disorder
in Europe with the cost of depression corresponding to
1% of the total economy of Europe (2). There are different
approaches and strategies to treat MDD in adults, ranging
from pharmacological and psychotherapeutic interventions
to transcranial magnetic stimulation, electroconvulsive
therapy to deep brain stimulation (3). The “National
Institute for Health and Care Excellence (NICE)” guideline
recommends antidepressant pharmacotherapy as a crucial
pillar in the treatment for all patients with moderate to severe
depression (3).
The choice of a particular antidepressant agent for an
individual patient currently is based on treatment guidelines,
experience, individual medical comorbidities, but unfortunately
largely based on “trial and error” (4). Despite decades of research
and international efforts to collect cohorts for genetic studies, we
still lack a fundamental understanding of the pathophysiology
for any of the classical psychiatric disorders, including MDD.
In other therapeutic areas such as diabetes or heart diseases
(5) a considerable proportion of hits for disease-associated
genes in genome-wide association studies (GWAS) match with
the targets of already marketed drugs. Precision medicine
and individualized therapy has dramatically and successfully
improved both our understanding and the treatment of certain
somatic diseases. For example, 5-year-survival in children with
acute lymphatic leukemia increased from 10% in 1990 to
90% in 2005 (6). Unfortunately, the situation is completely
different in neuroscience research, where one conspicuous
observation from the genetics of depressive disorders is that
none of the scores of candidates from GWAS involves the usual
psychopharmacologic suspects, i.e., monoamine transporters
or their receptors (4). Importantly, the genetic risk variants
identified thus far cover a broad spectrum of biological processes
but are enriched in neurodevelopmental or synaptic genes.
Taken together, these results point to new pathways involved in
pathophysiology, suggesting an entirely new biology for mental
disorders and the urgent need to reconsider mental illnesses
as “syndromes of disrupted neural, cognitive, and behavioral
systems” (7).
But how do we move from genomic variants to better
treatments? Before conceptually novel and improved treatment
strategies can be envisioned, we urgently need to focus
on a more precise understanding of the neurobiological
mechanisms underlying mental disorders and individual patient
response to pharmacotherapy by appropriate translational
approaches. Currently only a limited number of preclinical
or truly translational investigations is dedicated to the
issue of heterogeneity seen in response to antidepressant
treatment.
Frontiers in Psychiatry | www.frontiersin.org
The large heterogeneity in response to antidepressant treatment
(8) is a major problem in depression treatment. Although
the available treatments are safe, both psychiatrists and
patients have to cope with a considerable variability in
antidepressant treatment outcome: 20–30% of the patients
treated with antidepressant drugs fail to respond to two or more
pharmacological interventions (9). There are no biomarkers
available monitoring treatment response, disease state, or
predicting individual response to a particular compound (10).
Thus, the most effective antidepressant medication for each
patient can presently only be identified through trial and error
and needs several weeks to test for each given compound. If
early on we could predict that a chosen medication will likely
be ineffective for an individual patient, we could dramatically
reduce costs and patient suffering and increase treatment efficacy.
Therefore, the identification of individual factors predicting
treatment response is one of the most pressing needs in
depression treatment. Predictive biomarkers or biosignatures
would not only allow to predict or monitor treatment response in
clinical practice with marketed drugs but could—if compoundindependent—also assist in the evaluation of drug actions of
novel compounds at an early stage in clinical trials which are
frequently marred by late attrition. This is even more important
as over the last decades, encouraging preclinical evidence using
animal models pointed to innovative pharmacological targets to
treat MDD, such as antagonists of the corticotropin releasing
hormone receptor type 1 (11) or substance P receptor antagonists
(12). These compounds have entered clinical trials with high
hopes for a breakthrough in depression treatment, but they have
failed to show convincing results. These failures have called into
question as to how well traditional animal models for depression
can translate to clinical efficacy (13).
Altogether, this illustrates the urgent need to develop
improved translational models to better understand the
neurobiological mechanisms that underlie MDD and to more
specifically assess response to antidepressant treatment. We
here review recent progress and highlight some of the best leads
to diversify and improve discovery end points for preclinical
depression research and treatment response in nonhuman
organisms.
ANIMAL EXPERIMENTAL APPROACHES
TO MODEL DEPRESSION AND
ANTIDEPRESSANT TREATMENT
EFFICACY: INDIVIDUALITY MATTERS
Why should we use animals to model complex diseases like
MDD? What could be the strengths of an animal model, and what
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October 2018 | Volume 9 | Article 512
Herzog et al.
Understanding and Predicting Antidepressant Response
have been proven to be effective in general. Unfortunately, the
enigma of heterogeneity in antidepressant response has not
been systematically addressed to date although it has long being
recognized as one of the critical factors hampering antidepressant
drug discovery, clinical evaluation, and approval of potentially
novel compounds. Therefore to pave the way for so-called
precision psychiatry, we would like to propose a framework for
translational studies into individualized medicine in psychiatry.
Individuality—commonly defined as the collection
of divergent behavioral and physiological traits among
individuals—develops when unique environmental influences
act on the genome, following complex routes, to produce
phenotypic diversity. Individuality is considered central to the
development of several neuropsychiatric disorders. Focusing on
individuality rather than average outcomes has gained more and
more attention both in rodents (20) and in zebrafish (21).
Approaches to focus on heterogeneity and individuality
within a cohort of mice have been quite successfully used in
the context of stress research to identify putative neurobiological
pathways modulating individual susceptibility and resilience: In
2007, the Nestler group published the results of a groundbreaking
study, where they did not analyze the mere effect of a
certain manipulation (in this case a chronic social defeat stress
paradigm), but stratified each individual mouse based on its
performance in a defined behavioral test of social interaction
as outcome (22). Stratification of the animals based on their
performance in the social interaction test allowed to focus on
the differences within the experimental group, accompanied by
the advantage that the two new “extreme” subgroups (above
or below a certain threshold) become more homogeneous
(22), which might facilitate the discovery of true candidates.
In resilience research, this stratification approach has proven
successful in a number of excellent publications during the last
years (23–25). Aiming at the identification of the neurobiological
mechanisms underlying response to antidepressant treatment,
we recently established an animal experimental approach using
stratification into extreme subpopulations out of a considerably
large number of inbred, genetically homogeneous mice in
response to antidepressant treatment [Figure 1, (26)]. In addition
to the significant average group effect between antidepressant
and vehicle-treated groups, we continued to select, out of the
cohort of paroxetine-treated animals, subpopulations of good,
and poor responders based on their outcome in one of the
major behavioral tests assessing antidepressant-like efficacy in
rodents. Indeed, we were able to identify specific transcriptome
signatures associated with response status in murine blood and
to successfully translate and validate the findings from our
animal model in a cohort of depressed patients (26). Finally,
we could reveal a particular role of the glucocorticoid receptor
(GR) in shaping response to antidepressants, which is even more
interesting considering that those data have been generated by
an animal model using a hypothesis-free approach. The putative
role of the GR in modulating antidepressant-like effects had been
suggested already earlier by means of hypothesis-driven basic
and clinical depression research [for review: (27)], supporting
the validity of our model. We believe that this was the first step
toward a more in-depth and dimensional analysis of different and
are its limitations? From a psychiatrist’s point of view, it is difficult
to agree that rodent or even species such as zebrafish could be of
value to investigate a complex mental disorder characterized by
a set of diverse symptoms such as MDD. The same holds true
for the issue of response to psychopharmacological treatment.
A large heterogeneity in the symptomatology of MDD and a
close association with other comorbid psychiatric disorders in
a substantial proportion of MDD patients are major drawbacks
and confounding factors for clinical studies (14). The exclusive
use of peripheral tissue like blood can only be of limited value
in deciphering the neurobiology of depression, as the brain can
only be accessed indirectly, e.g., by neuroimaging approaches
(14). In addition, human post-mortem brain samples suffer
from many confounding variables like variation in pH, molecule
degradation, age bias, and a bias toward suicide victims (14).
In contrast, animal models offer unique advantages such as
high level of standardization. Working with standardized animal
cohorts can help to minimize biases, to deal with larger sample
sizes, e.g., when dealing with small, cost-efficient species such as
zebrafish and finally, they allow unrestricted access to the organ
of interest, i.e., the central nervous system (14–16).
The potency of an animal model can be described based
on three key elements: construct, face, and predictive validity
(17, 18). Construct validity is present in MDD animal models,
if depressive-like behavior and associated features can be clearly
and unambiguously seen and interpreted in the model (17). The
criterion of face validity is met if the model possesses similar
or comparable elements in terms of “anatomical, biochemical,
neuropathological or behavioral features” between animal and
human (18). Predictive validity focuses on the ability of an
animal model to serve as a tool for pharmacological research:
Antidepressant agents, which induce antidepressant-like effects
in animals, should also show similar or comparable effects in
humans (17, 18). Based on these criteria, the strength of an
animal model system can be estimated. Behavioral aspects of
MDD-related phenotypes as well as…
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