Only a few innovations have been made in recent decades with regard to psychiatric, and particularly antidepressant, drugs (Insel et al., 2006) (Figure 1). This conundrum reflects, at least partly, the lack of understanding of the disease biology. This poses a challenge not only to inventive drug development, but also to clinical practice, which faces remission rates of 30 per cent and less in patients given state-of-the-art pharmacological treatment for major depressive disorders (Trivedi et al., 2006). The situation is further aggravated by the exclusive current use of clinically based diagnostic criteria for major depression, which some critics view as ‘a pseudo-category, effectively homogenizing multiple expressions of depression’ (Parker, 2004).
Only a few innovations have been made in recent decades with regard to psychiatric, and particularly antidepressant, drugs (Insel et al., 2006) (Figure 1). This conundrum reflects, at least partly, the lack of understanding of the disease biology. This poses a challenge not only to inventive drug development, but also to clinical practice, which faces remission rates of 30 per cent and less in patients given state-of-the-art pharmacological treatment for major depressive disorders (Trivedi et al., 2006). The situation is further aggravated by the exclusive current use of clinically based diagnostic criteria for major depression, which some critics view as ‘a pseudo-category, effectively homogenizing multiple expressions of depression’ (Parker, 2004).
Only a few innovations have been made in recent decades with regard to psychiatric, and particularly antidepressant, drugs (Insel et al., 2006) (Figure 1). This conundrum reflects, at least partly, the lack of understanding of the disease biology. This poses a challenge not only to inventive drug development, but also to clinical practice, which faces remission rates of 30 per cent and less in patients given state-of-the-art pharmacological treatment for major depressive disorders (Trivedi et al., 2006). The situation is further aggravated by the exclusive current use of clinically based diagnostic criteria for major depression, which some critics view as ‘a pseudo-category, effectively homogenizing multiple expressions of depression’ (Parker, 2004).
This webinar showcases the Growth Direct System; an RMM (Rapid Microbial Method) that improves on traditional membrane filtration, delivering increased accuracy, a faster time to result, enhanced data integrity compliance, and more control over the manufacturing process.
Key learning points:
Understand the benefits of full workflow microbiology quality control testing automation in radiopharmaceutical production
Learn about ITM’s implementation journey and considerations when evaluating the technology
Find out how the advanced optics and microcolony detection capabilities of Growth Direct® technology impact time to result (TTR).
Don’t miss your chance to learn from experts in the industry –Register for FREE
Can’t attend live? No worries – register to receive the recording post-event.
In this situation, it may not come as a surprise that biological markers (biomarkers) associated with depression in one scientific article cannot be reproduced in subsequent studies. Ultimately, the core problem when searching for biomarkers is the unsatisfactory clinical description of patients, which is a prerequisite to making a robust and reproducible association between biological markers and the clinical phenotype.
It is anticipated that a revision of the current diagnostic classification for psychiatric disorders (the fifth Diagnostic and Statistical Manual, DSM-V) will consist of a more detailed clinical description of patients, including the course of the disease; the context in which it occurs; the familial background and comorbid disorders. Also, there is hope that the new classification will consider neurobiological features. However, availability of a revised diagnostic manual is not expected before 2011 – quite possibly even later.
The decision was therefore made to begin implementing a systematic biomarker discovery program that aims to achieve the following:
Improve the link between clinical and preclinical data
Help determine the optimal dose
Obtain the best target populations for human Proof-of-Concept (PoC) and subsequent studies
From the onset a clarification of definitions is essential: The NIH working group has defined a biomarker as a characteristic that is objectively measured and evaluated as an indicator of normal biologic processes, pathogenic processes or pharmacologic responses to a therapeutic intervention (Biomarkers Definitions Working Group, 2001). Functional categories include exposure, safety and efficacy biomarkers. If an efficacy marker can substitute for a clinical endpoint, it can serve as a surrogate endpoint in clinical trials.
In our program the biomarker search is based primarily, but not exclusively, on assessment of physiological measures (under both baseline conditions and in challenge paradigms), transcription patterns (from target organs (CNS or other for safety issues) and peripheral cells), genotypes and neuroimaging, including both target occupancy and functional read-outs.
To strengthen the link between preclinical and clinical data, with regard to both safety and efficacy aspects, animal models to assess novel drug candidates should share at least some core biological features of the human disorder. This may be particularly relevant when testing compounds with a novel mechanism-of-action, as these may require an altered tone to show any efficacy. On the other hand, animal disease state models that show some biological alterations also observed in a subpopulation of patients with depression will guide selection of the target population in clinical development later on.
In view of the lack of consensus on biological markers for the majority of psychiatric disorders, without consideration of the subtypes of disorders, we have initiated exploratory studies together with clinical development teams and academic investigators around the world (Figure 3). The aim of these studies is to associate distinct biomarkers with a specific clinical phenotype. This can only be achieved if state-of-the-art technology is employed and the subjects, including both healthy and symptomatic volunteers (or patients), are thoroughly phenotyped using appropriate self- and physician-rated scales. The dimensional aspect of psychiatry opens up the possibility of exploring biomarkers in unsymptomatic (healthy) volunteers, in whom known and presumed risk factors for certain disorders are documented. To ensure the relevance of such exploratory analyses it is important to assess a large number of well-characterised subjects, confirm findings and employ sophisticated statistical approaches (e.g. pattern recognition).
Patterns identified in control subjects are used to generate hypotheses that are tested in acutely ill, but untreated, patients with different, well characterised clinical phenotypes. This approach is pursued with dependable clinical investigators who recognise the need to profile patients thoroughly at the symptom and context level. The drawback of the often small sample size in academic collaborations is offset by the detailed clinical information. This allows generation of hypotheses that may be tested in larger clinical trials. In the latter, the detailed clinical characterisation is again paramount to confirm subgroups that have been detected in the smaller studies. Larger clinical trials that examine treatment effects will then enable selection of biomarkers that are indicative of a specific disease state, and those that are associated with a good treatment response. These latter biomarkers, upon confirmation, can then be proposed as surrogate endpoints.
The importance of pattern analysis also for safety aspects is supported by the rising literature on toxicogenomics and attempts to associate classical toxicological markers with gene expression patterns (Ekins, 2006). Another aspect of pattern analysis with regard to safety and tolerability is that thorough pharmacodynamic profiling of drug candidates can also help to select target populations; for example, while activation of the stress axis may be an unwanted effect in some psychiatric patients, it may be a useful drug effect for other psychiatric populations. Such drug and population profiling will help to select the right drug in its optimal dose for the best patient population.
To ensure optimal use of biomarker data generated in such clinical studies, an iterative communication between discovery, clinical pharmacology and clinical development must be maintained. This process means to also actively address barriers to a successful integration, such as perceived risks and burden by the clinical teams, inappropriate funding and a late start to biomarker assessment in drug development. In our hands, biomarkers are part of the discovery process as soon as a drug candidate is nominated. Strictly speaking, our program starts even earlier, as it seeks to associate a panel of biomarkers with a specific disease biology even before a drug effect is explored. We are therefore assessing biomarkers systematically in healthy volunteers and untreated patients, to identify markers of vulnerability, resilience and different disease states, not confounded by treatment. At the same time, this approach provides useful information for mechanistic read-outs in animal models. We hope that this strategy will not only improve the predictive value of animal models for future drug development, but that it will also identify novel drug targets by determining resilience markers.
In psychiatry this seems particularly pertinent as clinicians have very limited tools to predict who will develop which disorder, who will respond to a given treatment and who will need long-term treatment for prevention of a new episode. As many academic investigators and clinicians are painfully aware of this problem, studies are being conducted around the globe to identify objective read-outs that help with the diagnosis as well as with selection of treatment. As academia is under pressure to publish, confirmation and reproducibility of results is not always a main focus of published work. For pharmaceutical companies, in turn, reproducible and reliable data are of utmost importance, if a development program is to be based on such data. Therefore, public-private collaborations can help to generate data that withstand the required scrutiny. Ultimately, the goal for all the stakeholders involved should be to move towards personalised medicine, aiming to have tools at hand that allow the selection of the right drug for the right patient at the right time.
References
1. Biomarkers Definitions Working Group (2001): Biomarkers and surrogate endpoints: preferred definitions and conceptual framework. Clin Pharmacol Ther 69: 89-95.
2. Ekins S (2006): Systems-ADME/Tox: resources and network approaches. J Pharmacol Toxicol Methods 53: 38-66.
3. Insel TR, Scolnick EM (2006): Cure therapeutics and strategic prevention: raising the bar for mental health research. Mol Psychiatry 11: 11-17.
4. Parker G (2004): Evaluating treatments for the mood disorders: time for the evidence to get real. Aust N Z J Psychiatry 38: 408-414.
5. Trivedi MH, Rush AJ, Wisniewski SR, Nierenberg AA, Warden D, Ritz L et al (2006): Evaluation of outcomes with citalopram for depression using measurement-based care in STAR*D: implications for clinical practice. Am J Psychiatry 163: 28-40.
This website uses cookies to enable, optimise and analyse site operations, as well as to provide personalised content and allow you to connect to social media. By clicking "I agree" you consent to the use of cookies for non-essential functions and the related processing of personal data. You can adjust your cookie and associated data processing preferences at any time via our "Cookie Settings". Please view our Cookie Policy to learn more about the use of cookies on our website.
This website uses cookies to improve your experience while you navigate through the website. Out of these cookies, the cookies that are categorised as ”Necessary” are stored on your browser as they are as essential for the working of basic functionalities of the website. For our other types of cookies “Advertising & Targeting”, “Analytics” and “Performance”, these help us analyse and understand how you use this website. These cookies will be stored in your browser only with your consent. You also have the option to opt-out of these different types of cookies. But opting out of some of these cookies may have an effect on your browsing experience. You can adjust the available sliders to ‘Enabled’ or ‘Disabled’, then click ‘Save and Accept’. View our Cookie Policy page.
Necessary cookies are absolutely essential for the website to function properly. This category only includes cookies that ensures basic functionalities and security features of the website. These cookies do not store any personal information.
Cookie
Description
cookielawinfo-checkbox-advertising-targeting
The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Advertising & Targeting".
cookielawinfo-checkbox-analytics
This cookie is set by GDPR Cookie Consent WordPress Plugin. The cookie is used to remember the user consent for the cookies under the category "Analytics".
cookielawinfo-checkbox-necessary
This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Necessary".
cookielawinfo-checkbox-performance
This cookie is set by GDPR Cookie Consent WordPress Plugin. The cookie is used to remember the user consent for the cookies under the category "Performance".
PHPSESSID
This cookie is native to PHP applications. The cookie is used to store and identify a users' unique session ID for the purpose of managing user session on the website. The cookie is a session cookies and is deleted when all the browser windows are closed.
viewed_cookie_policy
The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It does not store any personal data.
zmember_logged
This session cookie is served by our membership/subscription system and controls whether you are able to see content which is only available to logged in users.
Performance cookies are includes cookies that deliver enhanced functionalities of the website, such as caching. These cookies do not store any personal information.
Cookie
Description
cf_ob_info
This cookie is set by Cloudflare content delivery network and, in conjunction with the cookie 'cf_use_ob', is used to determine whether it should continue serving “Always Online” until the cookie expires.
cf_use_ob
This cookie is set by Cloudflare content delivery network and is used to determine whether it should continue serving “Always Online” until the cookie expires.
free_subscription_only
This session cookie is served by our membership/subscription system and controls which types of content you are able to access.
ls_smartpush
This cookie is set by Litespeed Server and allows the server to store settings to help improve performance of the site.
one_signal_sdk_db
This cookie is set by OneSignal push notifications and is used for storing user preferences in connection with their notification permission status.
YSC
This cookie is set by Youtube and is used to track the views of embedded videos.
Analytics cookies collect information about your use of the content, and in combination with previously collected information, are used to measure, understand, and report on your usage of this website.
Cookie
Description
bcookie
This cookie is set by LinkedIn. The purpose of the cookie is to enable LinkedIn functionalities on the page.
GPS
This cookie is set by YouTube and registers a unique ID for tracking users based on their geographical location
lang
This cookie is set by LinkedIn and is used to store the language preferences of a user to serve up content in that stored language the next time user visit the website.
lidc
This cookie is set by LinkedIn and used for routing.
lissc
This cookie is set by LinkedIn share Buttons and ad tags.
vuid
We embed videos from our official Vimeo channel. When you press play, Vimeo will drop third party cookies to enable the video to play and to see how long a viewer has watched the video. This cookie does not track individuals.
wow.anonymousId
This cookie is set by Spotler and tracks an anonymous visitor ID.
wow.schedule
This cookie is set by Spotler and enables it to track the Load Balance Session Queue.
wow.session
This cookie is set by Spotler to track the Internet Information Services (IIS) session state.
wow.utmvalues
This cookie is set by Spotler and stores the UTM values for the session. UTM values are specific text strings that are appended to URLs that allow Communigator to track the URLs and the UTM values when they get clicked on.
_ga
This cookie is set by Google Analytics and is used to calculate visitor, session, campaign data and keep track of site usage for the site's analytics report. It stores information anonymously and assign a randomly generated number to identify unique visitors.
_gat
This cookies is set by Google Universal Analytics to throttle the request rate to limit the collection of data on high traffic sites.
_gid
This cookie is set by Google Analytics and is used to store information of how visitors use a website and helps in creating an analytics report of how the website is doing. The data collected including the number visitors, the source where they have come from, and the pages visited in an anonymous form.
Advertising and targeting cookies help us provide our visitors with relevant ads and marketing campaigns.
Cookie
Description
advanced_ads_browser_width
This cookie is set by Advanced Ads and measures the browser width.
advanced_ads_page_impressions
This cookie is set by Advanced Ads and measures the number of previous page impressions.
advanced_ads_pro_server_info
This cookie is set by Advanced Ads and sets geo-location, user role and user capabilities. It is used by cache busting in Advanced Ads Pro when the appropriate visitor conditions are used.
advanced_ads_pro_visitor_referrer
This cookie is set by Advanced Ads and sets the referrer URL.
bscookie
This cookie is a browser ID cookie set by LinkedIn share Buttons and ad tags.
IDE
This cookie is set by Google DoubleClick and stores information about how the user uses the website and any other advertisement before visiting the website. This is used to present users with ads that are relevant to them according to the user profile.
li_sugr
This cookie is set by LinkedIn and is used for tracking.
UserMatchHistory
This cookie is set by Linkedin and is used to track visitors on multiple websites, in order to present relevant advertisement based on the visitor's preferences.
VISITOR_INFO1_LIVE
This cookie is set by YouTube. Used to track the information of the embedded YouTube videos on a website.