Psychopharmacological Drug Development in A Depression?by Thomas Hartmann, PhD | May 14, 2013
“If you are a mouse and suffer from depression, we can cure you!”.
You may have heard similar statements for other diseases, which is a general reflection on the current state of drug development. After spending billions of dollars in pharmaceutical drug development only about 30 new drugs reached the market last year — a number that is higher than in previous years, but still. It’s not good news for patients, especially those suffering from mental illness, for whom the outlook on new drugs is even bleaker. Why the dry pipeline?
Mental diseases are difficult to treat. Drug development in particular is hampered by the following key obstacles:
Firstly, the cause of most mental diseases at the molecular level is often unknown, making it difficult to tackle a target with a pill. In oncology for example, molecular targets have been identified and in certain cases, a single point mutation in a gene has been identified as the cause for the disease. Subsequently, specific drugs have been developed against that target and patients are screened for specific mutations to determine whether they are suitable candidates for that treatment or not.
Unfortunately this is not the case for many mental disorders. The first generation of approved antidepressants was found through serendipity by observant physicians. After that, a few more classes of antidepressants emerged, mostly reducing the undesirable side effects associated with first generation drugs. Nevertheless, the exact mode of action of most antidepressants in the human brain remains unclear.
Variable drug response
Secondly, as with any drug, not all patients respond equally to a particular treatment. In a 9-year study investigating the quality of care in patients with depression the percentage of non-responders was around 25%. More pessimistic estimates put the number as high as 50%. It is not uncommon for patients with depression to try several antidepressants before they find one that works or before they give up on it.
Within the last five years, a debate on the efficacy of antidepressants has emerged. One study accused the publishing bias towards positive trial data of skewing our views on the therapeutic effectiveness of antidepressants. The researchers looked back at 74 studies with 12,564 registered patients that were reported to the FDA. 38 of these studies were considered to be positive by the FDA. All of the positive studies were published, except one. In contrast, only a third of the studies, which were considered negative or ambiguous, were published. Even then negative data were misrepresented, according to the investigators.
Similar conclusions were drawn in a recent study on antipsychotic drugs. This is not to say that antidepressants or antipsychotic drugs don’t work. We may have just overestimated their effectiveness. The debate highlights the difficulty in finding effective pills with our current medical knowledge and empirical approach.
The difficulty of using animal models
Thirdly, in the pre-clinical stage of drug development, animal models are used to screen for toxicity and efficacy. The crux here is in the word “model”. No animal model is perfect. There is an inherent risk for failure when the drugs are finally administered to humans. These failures are dramatic for patients, and costly for the industry. Recent failures of late-stage drugs developed against schizophrenia, pain and Alzheimer’s disease are testimony to that statement.
Then what’s the solution?
Changes to the drug development paradigm seem to be required. A better understanding of pathological mechanisms and therapeutic effects is needed. Most notably, “personalized approaches that use biomarkers, including neurophysiological, neuroimaging, genetic, and neuropsychological techniques, are required to guide treatment”. Broadening the scope of clinical trials is already commonplace for drugs in other disease areas. Although costly, such an extension is feasible and of merit.
Also, the Brain Activity Map program seems to have traction. The project is a coordinated effort of government agencies, universities, and the private sector to create a comprehensive understanding of the brain’s function. Similar to the Human Genome Project, the approach would start with the investigation of simple organisms leading up to the human species. Obviously, the challenges are enormous, but one of the outcomes of that project could be a better understanding of the normal brain function leading to better ways of treat mental disease.
Promising technologies such as induced pluripotent stem cells, in vitro neuronal circuits, and connectomics will be discussed in an upcoming workshop at Institute of Medicine of the National Academy of Sciences. The title of this workshop is “Accelerating Therapeutic Development for Nervous System Disorders towards First-in-Human Trials”. Not only scientific tools but also regulatory and ethical issues are key topics of the meeting agenda. The problems have been recognized. The search for solutions is on. There is light at the end of the tunnel.
Alivisatos AP, Chun M, Church GM, Deisseroth K, Donoghue JP, Greenspan RJ, McEuen PL, Roukes ML, Sejnowski TJ, Weiss PS, & Yuste R (2013). The Brain Activity Map. Science (New York, N.Y.) PMID: 23470729
Fullerton CA, Busch AB, Normand SL, McGuire TG, & Epstein AM (2011). Ten-year trends in quality of care and spending for depression: 1996 through 2005. Archives of general psychiatry, 68 (12), 1218-26 PMID: 22147841
Mathew, S., & Charney, D. (2009). Publication Bias and the Efficacy of Antidepressants American Journal of Psychiatry, 166 (2), 140-145 DOI: 10.1176/appi.ajp.2008.08071102
Turner EH, Knoepflmacher D, & Shapley L (2012). Publication bias in antipsychotic trials: an analysis of efficacy comparing the published literature to the US Food and Drug Administration database. PLoS medicine, 9 (3) PMID: 22448149
Turner EH, Matthews AM, Linardatos E, Tell RA, & Rosenthal R (2008). Selective publication of antidepressant trials and its influence on apparent efficacy. The New England journal of medicine, 358 (3), 252-60 PMID: 18199864
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