As doctors and patients encountered the inconsistent performance of these drugs in their offices and clinics, they tried all those strategies mentioned above in non-responders [labeled as having treatment resistant depression] and anecdotal solutions abounded [along with the rise of poly·pharmacy, a former no-no]. That future history of the second age of psycho·pharmacology will undoubtedly have a figure like the one on the right showing what might be called the Texas Many-Step, a series of studies that attempted to systematize these anecdotal sequencing, combining, augmenting strategies as algorithms. The studies were publicly funded, expensive, and notoriously failed enterprises. TMAP, the opening gambit, was a drug company encouraged scam to get expensive drugs into the public sector – now defunct and still in the courts. The middle three were NIMH funded duds. And the final one is an ongoing NIMH study [AKA dud-in-training]. The history book chapter describing this period might be entitled squeezing blood from a turnip? I’ve chased off readers with my preoccupation with these studies, so I’ll skip the details [put any of them into search below if you need a refresher].
Brain imaging predictors and the international study to predict optimized treatment for depression: study protocol for a randomized controlled trialby Stuart M Grieve, Mayuresh S Korgaonkar, Amit Etkin, Anthony Harris, Stephen H Koslow, Stephen Wisniewski, Alan F Schatzberg, Charles B Nemeroff, Evian Gordon, and Leanne M WilliamsTrials. 2013; 14: 224.
Background: Approximately 50% of patients with major depressive disorder [MDD] do not respond optimally to antidepressant treatments. Given this is a large proportion of the patient population, pretreatment tests that predict which patients will respond to which types of treatment could save time, money and patient burden. Brain imaging offers a means to identify treatment predictors that are grounded in the neurobiology of the treatment and the pathophysiology of MDD.Methods/Design: The international Study to Predict Optimized Treatment in Depression is a multi-center, parallel model, randomized clinical trial with an embedded imaging sub-study to identify such predictors. We focus on brain circuits implicated in major depressive disorder and its treatment. In the full trial, depressed participants are randomized to receive escitalopram, sertraline or venlafaxine-XR [open-label]. They are assessed using standardized multiple clinical, cognitive-emotional behavioral, electroencephalographic and genetic measures at baseline and at eight weeks post-treatment. Overall, 2,016 depressed participants [18 to 65 years old] will enter the study, of whom a target of 10% will be recruited into the brain imaging sub-study [approximately 67 participants in each treatment arm] and 67 controls. The imaging sub-study is conducted at the University of Sydney and at Stanford University. Structural studies include high-resolution three-dimensional T1-weighted, diffusion tensor and T2/Proton Density scans. Functional studies include standardized functional magnetic resonance imaging [MRI] with three cognitive tasks [auditory oddball, a continuous performance task, and Go-NoGo] and two emotion tasks [unmasked conscious and masked non-conscious emotion processing tasks]. After eight weeks of treatment, the functional MRI is repeated with the above tasks. We will establish the methods in the first 30 patients. Then we will identify predictors in the first half [n = 102], test the findings in the second half, and then extend the analyses to the total sample.
Acknowledgements: We acknowledge Brain Resource as the sponsor for the iSPOT-D study [NCT00693849]. Claire Day [Global Study Co-ordinator] is thanked for her pivotal role in making this study happen, and for her boundless enthusiasm and energy. We thank the iSPOT-D Executive Committee for their valuable input into this manuscript and into the study overall [Members: A John Rush [Chair], Lea Williams [Academic PI], Steve Koslow, AmitEtkin, Evian Gordon, Steve Koslow, Stephen R Wisniewski. We also thank those responsible for designing the iSPOT-D protocol [A. John Rush, Lea M Williams, Evian Gordon, Charles B Nemeroff, Alan F Schatzberg]. We gratefully acknowledge the editorial support of Jon Kilner, MS, MA [Pittsburgh, PA, USA]. SMG acknowledges the support of the Sydney University Medical Foundation. Dr Lavier Gomes, Ms Sheryl Foster and the Department of Radiology at Westmead are thanked for their substantial contributions to MRI data acquisition.
Churning:These studies produced a astounding number of articles. The hands-down winner of all times was STAR*D with well over a hundred, but the others were like this one – lots of intermediate articles. In spite of the voluminous ouptput, definitive reporting or formal compliance with clinical trial reporting was missing in action.
Ghost:Jon Kilmer, science fiction author and medical ghost writer, has been in the background for the long haul. I think these many articles make up the majority of his career as a medical writer.
Authors:Lead by A. John Rush and Madhukar Trivedi, a stable of other authors make up the cohort that has been the river that runs through all these studies, mixed and matched as the papers continued to pour into the literature – a who’s who of the PHARMA friendly psychiatric KOLs. Whether funded by the NIMH or by the Brain Resource group in Australia, you can count on familiar names – many familiar names. And they’re joined by even more KOLs in EMBARC and iSPOT – many listed in both projects
A Database [at last we reach the actual topic of this post]:Except for iSPOT, these were publicly funded projects that produced reams of data, and the grandaddy, STAR*D, made much at it’s outset of its creating a database for further study by all. This database has mainly been tapped by the original gaggle of authors, but a few others have waded in. iSPOT came with the Brain Resources Group making a similar offer, a vast database made available for mining by all. This was to be, in both cases, the dawn of data sharing – opening the vaults to the scientists of the world.
While none of these studies had anything directly to do with PHARMA, they all have everything to do with PHARMA, and a devoutly sought new industry – biological testing in psychiatry – encoded in the also becoming-passé topic, personalized medicine. But at least STAR*D and iSPOT advertised that the generated data would be available to all. With the front running issue now being data transparency and the pharmaceutical industry trying to get away with substituting data sharing while maintaining their former tight control and continuing to hold their cards close to the chest, these tired studies offer us some insight to the whole story of available data. How did the NIMH and Brain Resource handle the issue of the advertised data access? Are STAR*D and friends candidates for a RIAT team – abandoned studies in need of restoring? These are important right-now questions.