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Personalized Medicine in Psychiatry: from DSM to brain-based RDoC, iSPOT-D and biomarkers

(Editor’s Note: this is Part 2 of the new 3-part series written by Dr. Evian Gordon drawing from his participation at the Personalized Medicine World Congress on January, 23, 2012 at Stanford University.)

Most Personalized Medicine research in Psychiatry using molecular measures alone have failed to replicate. Whilst disappointing, this is not surprising, since 80% of human 25,000 genes have some effect on the brain.

There are therefore growing efforts expanding Genomic Biomarkers in Psychiatry to Neuroimaging (all Brain-based biological and cognitive measures). Some approaches target Gene+Brain Combination Biomarkers to specific neurotransmitter “Circuits. For example the Serotonin circuit: since SSRIs impact upon this Amygdala-Anterior Cingulate-Raphe Nucleus–BDNF molecular modulation circuit.

Whatever the approach, the goal is to shed light on biologically relevant characteristics of the patient and then increasingly elucidate which of these predict a specific treatment response.

Diverse groups are shaping this biomarker landscape for psychiatry, including DSM-5, NIMH and many translational neuroscience groups.

I presented a distillation of these in my PMWC presentation:

  • The need for neurobiological information to “validate” constructs of Psychiatric disorders;
  • The need for multiple complementary Gene+Brain measures across scale;
  • A Neurodevelopmental context, with emphasis on the age of onset and peak periods when Psychiatry instabilities manifest;
  • A Dimensional context of Psychiatric Disorders, where underlying neurobiology is continuous from normality to disorder;
  • Large Databases of norms for age and longitudinal clinical outcomes with clear end-points that can confirm/disconfirm Biomarker treatment predictions;
  • A translational approach that links basic mechanism of Psychopathology to targeted Biomarkers that predict who is most likely to respond best to what intervention.

These emphasizes are reflected in the move to the DSM-5 and the “Research Domain Research Criteria (RDoC) initiatve launched by the National Institute of Mental Health.

The goal of DSM-5 to expand “signs and symptoms” classification to incorporate biological measures, is captured to some extent by this quote from Task Force Chair, David Kupfer:

“As we gradually build on our knowledge of mental disorders, we begin bridging the gap between what lies behind us (presumed etiologies based on phenomenology) and what we hope lies ahead (identifiable pathophysiologic etiologies).” American J Psychiatry, 168 (7): 672-674, 2011.

The DSM provides a consistent framework for clinical communication. But it was developed at a time of limited knowledge in brain science. Its current limitation in Personalized treatment prediction reflects this knowledge gap and the growing call for fleshing out the clinical reality of co-morbidity and heterogeneity by connecting them with meaningful biological entities.

RDoC is a complementary approach (Insel et al. 2010. American J Psychiatry, 1020; 167 (7): 748-751). One of its goals will be to inform new editions of classification systems. But the breadth of its implications constructively challenges all current conventional approaches in Psychiatry.

RDoC is a Framework of 5 organizing ‘Domains’ that cut across traditional disorder classifications. The Research Domain Criteria (RDoc) are:

Negative Valence Systems (Threat and Anxiety);
Positive Valence Systems (Rewards and Habits);
Cognitive Systems (attention, perception, working memory);
Systems for Social Processes (identification of facial expression, Theory of Mind);
Arousal/Regulatory Systems (State and Trait dynamics).

By explicating RDoC, NIMH seeks to elucidate the underpinning biological mechanisms across scale (from genes to circuits and behavior) of these 5 domains. This is anticipated to lead to new molecular and neuroimaging drug discovery targets, accelerate a new culture of research strategies (succeed or fail fast; standardization, integration, data sharing) and provide fundamentally new bio-behavioral approaches to classifying (and treating) mental disorders.

Using standardized methods to advance Personalized Medicine in Psychiatry

One approach to integrating biomarkers with clinical knowledge in Psychiatry is the use of standardized measurement to link each type of information.

Brain Resource has a flagship study underway that uses standardized methods to identify biomarkers of personalized treatment for depression: the international Study to Predict Optimized Treatment for Depression (iSPOT-D).

iSPOT-D uses the standard battery of measures established by Brain Resource, and used to set up the international database. These measures include:

Molecular (Genetic variants);
Brain Imaging (structural and functional scans) ;
Physiology (EEG, Evoked Potentials, heart rate and skin conductance);
Cognitive and Emotional behavior;
Self-reported experience.

iSPOT-D is using these measures to identify biomarkers that predict these end points:

  • Overall Response to the medication (No/Yes: defined by symptom remission);
  • Different responses to the 3 most commonly employed antidepressives in the U.S. (Escitalopram; Sertraline; Venlafaxine-XR);
  • Dose (For Example Venlafaxine: SSRI 150mg);
  • Side effects;
  • Long Term Remission.

These protocols have been published (For details, see: Williams, Rush, Koslow, Wisniewski, Cooper, Nemeroff, Schatzberg, Gordon, Trials, 4, 2011).

iSPOT-D will have 2,000 patients with Major Depression Disorder (MDD). The goal is to determine the best biomarkers for treatment prediction in the 3 medications. Data for the first 1,000 participants is currently in analysis.

The need for biomarkers in Psychiatry

The need for identifying biomarkers is enormous, and depression is a striking example of this need.

The cost of depression in the U.S. is $43.7 billion per year, and $83 billion when lost work productivity is factored in. Each year, over 60 million prescriptions are written for escitalopram, sertraline and venlafaxine-XR. Retails sales for these medications total $6 billion.

The example below serves to illustrate the implications of discovering Biomarkers that predict treatment response for the 3 most commonly used antidepressants in the U.S.:

Let’s assume the current 1 in 3 chance of being prescribed the right antidepressive treatment first time can be improved by 25% (from 33% to 42%). That means that around 2m people (10% of the 19m suffering depression in the U.S.) will benefit from saving at least one incorrect prescription. The iSPOT medications represent a 40% market share so that implies 800,000 people (assuming simple averages) will benefit from this saving. Assuming an average $50 prescription cost per patient, this implies a direct medication cost saving of $40m. This of course ignores the flow on costs of clinical consultation time, productivity losses and overall patient suffering when getting the wrong medication. Adding the cost of even 1 lost day and 1 inefficient clinical visit mean and these savings can increase ten fold (assuming $500 cost to an employer for one lost day and $100 for a clinical visit). It also ignores the benefit that a patient that reaches a solution faster will have higher compliance taking the medication and data has shown that this leads to overall reduced medical expenditures of more than $1,000 pa (“Recovery from Depression, Work Productivity, and Health Care Costs Among Primary Care Patients, Gregory E. Simon, MD, et al. General Hospital Psychiatry 22, 153-162, 2000).

Finding the right biomarkers for each person is an important way to get the right medication for each patient quickly.


To Be Continued…

New Series on Per­son­al­ized Med­i­cine and the Brain:

  • Mon­day, Feb­ru­ary 13th: The State of Per­son­al­ized Med­i­cine
  • Mon­day, Feb­ru­ary 20th: Per­son­al­ized Med­i­cine in Psychiatry (above)
  • Mon­day, Feb­ru­ary 27th: Work­ing with Health Care Indus­try Stake­hold­ers Towards Brain-based Per­son­al­ized Medicine
– Dr Evian Gor­don is the Exec­u­tive Chair­man of theBrain Resource Com­pany.  He ini­tially drew upon  his sci­ence and med­ical back­ground to estab­lish the inter­dis­ci­pli­nary Brain Dynam­ics Cen­ter, in 1986.  Through the Brain Dynam­ics Cen­ter and its col­lab­o­ra­tive net­works, Dr Gor­don estab­lished an “inte­gra­tive neu­ro­science” approach, grounded in the use of stan­dard­ized meth­ods across mul­ti­ple types of data. Using this approach, Dr Gor­don founded the “Brain Resource Com­pany”, that cre­ated the first inter­na­tional data­base on the human brain. The data­base is the asset which under­pins the devel­op­ment of new tools for brain health and its per­son­al­ized appli­ca­tion in the mar­ket, such as assess­ments of brain health, deci­sion sup­port sys­tems, and per­son­al­ized train­ing pro­grams. Brain Resource has also sup­ported the for­ma­tion of a non-profit 501c3 Foun­da­tion, called ‘BRAIN­net” (, through which sci­en­tists have access to many of these datasets for inde­pen­dent research.

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