Drug therapy is the accepted treatment for panic disorder, but many patients don’t respond at all. Could a personalised treatment approach help physicians prescribe the best drug for a given patient? Abi Millar finds out.
Panic disorder is not always an easy condition to treat. While drug therapy (often in combination with psychotherapy) is the first-line treatment, the medications on offer do not work for everyone, with patients exhibiting a high variability in response.
Generally speaking, they are offered one four classes of drug: selective serotonin reuptake inhibitors (SSRIs), selective norepinephrine-serotonin reuptake inhibitors (SNRIs), tricyclic antidepressants (TCAs), or benzodiazepines. The first two are usually the most effective, and the least likely to lead to side effects.
Unfortunately, in short-term clinical trials, between 17% and 64% of participants with panic disorder did not respond well to pharmacotherapy, and in the longer term, around 20-40% of patients fail to achieve full remission. On top of this, up to 30% finish treatment early because of side effects, and once their regimen is discontinued, between a quarter and half will relapse within six months. This means many sufferers remain seriously ill several years after diagnosis.
“In clinical practice, clinicians still proceed by a trial and error approach, without any evidence-based predictor or test to guide the choice,” says Dr Daniela Caldirola of the Department of Clinical Neurosciences at Hermanas Hospitalarias in Como, Italy. “Thus, there is a medical need to discover reliable predictors of psychotropic drug response and tolerability in each patient, according to his or her unique features.”
In other words, if pharmacologists can adopt a personalised approach to treatment – targeting the right drugs to the right patients – they will up their odds of success.
A debilitating condition
The need for adequate treatments is not in doubt. Panic disorder is highly prevalent, estimated to affect around 3-4% of people during their lifetimes. While women are more likely to be affected than men, and younger adults more than older adults or children, strictly speaking it can strike anyone at any time.
The condition, moreover, can be disabling, with the signature panic attacks proving emotionally and physically galling. Typically, sufferers live their lives in a state of high alert, avoiding the situations in which they feel most susceptible.
“Panic attacks are sudden episodes of intense fear and discomfort, associated with a surge of somatic symptoms such as chest pain, palpitations, dyspnea, and breathlessness,” says Caldirola. “The disorder causes marked distress and deterioration in quality of life, and may induce benzodiazepines or alcohol abuse and depression.”
While there is no unifying hypothesis to explain why panic attacks occur in some people and not others, it seems that neurobiological alterations (i.e., within the brain’s ‘fear circuit’) play a key role in mediating its course. Clearly, a better understanding of its biological underpinnings could lead to more effective drugs. But in the meantime, it may be possible to make better use of what is currently on offer.
The search for predictors
Earlier this year, Caldirola and colleagues conducted a comprehensive review of studies in the field, sifting through 1,306 studies to find 22 suitable for inclusion. These studies, which were randomised and placebo-controlled, looked into the effectiveness of three drugs: paroxetine, venlafaxine XR, and alprazolam.
Rather than exploring their efficacy per se, Caldirola’s team wanted to identify variables that might make a difference to treatment outcomes.
“A reliable consensus was lacking regarding which clinical predictors, if any, are worthy of being considered by clinicians when they prescribe medications for panic disorder,” says Caldirola. “Our approach allowed us to provide a quantitative review of the mixed data available on this topic.”
They were particularly interested in sociodemographic factors, such as gender and age, as well as clinical moderators like severity and comorbidities. Their paper, published in the journal Personalized Medicine in Psychiatry in April, is thought to be the first meta-analysis with this goal.
“The variables we chose are easily measurable with clinical interview and examinations. Thus, in the event of significant results, clinicians could have easily used these measures in clinical practice, before commencing treatment, as predictive tools to maximise therapeutic efficacy and minimise side effects of antipanic medications,” says Caldirola.
Unfortunately, the researchers did not find any straightforward predictive tools of this kind.
“We hoped to obtain better results. We found only limited support for the moderating effects of sociodemographic and clinical variables on the short-term pharmacotherapy for PD,” says Caldirola.
There were, however, a few correlations of note. Firstly, in trials for the SNRI venlafaxine, patients with a longer duration of treatment were most likely to benefit. Perhaps counterintuitively, this also applied to patients who had been ill for a long time. Secondly, in trials for the SSRI paroxetine, older patients were more likely to suffer side effects and drop out.
As Caldirola points out, it is important to be cautious in interpreting these results, as only a limited number of studies were suitable for inclusion.
“We cannot exclude that the paucity of studies might have influenced the results,” she says. “Available data allowed us to carry out our meta-analysis only on a few medications, and we cannot exclude that sociodemographic or clinical variables may influence the treatment outcomes of other drugs.”
Hope for the future
Despite these rather disappointing findings, Caldirola still holds out hope for a personalised approach to treatment. She believes that, while research efforts are at an early stage, it seems like the most promising means of improving outcomes.
“Currently, alternative strategies for optimising outcomes of pharmacotherapy in PD are not achievable,” she says. “Indeed, novel mechanism-based antipanic drugs are far from being implemented in clinical use, and no evidence supports the use of existing medications already approved for other psychiatric disorders in PD. Thus, the personalised approach in PD may help to increase the rate of responses to current recommended pharmacotherapy.”
Within psychiatry in general, the move towards personalised medicine has been slow. Since these conditions are typically diagnosed through symptoms, rather than through biological markers, it is hard to know with any accuracy which patients should receive which drugs.
In the future, however, personalised approaches to treatment may become the norm. Already, researchers have made strides with major depressive disorder (MDD), having isolated a number of biomarkers that could predict response to treatment. Caldirola feels that researchers in her own field need to follow suit.
“Similarly to what is being carried out in major depression, the search for predictors of response in panic disorder should be extended beyond clinical variables and include neurobiological functions, biomarkers, and genetic or pharmacogenetic characteristics,” she says. “To date, only limited investigations have explored biological predictors, and these have not been sufficient to provide reliable results.”
Data, data, data
Her research team is looking into exploring these biological predictors in more depth. For instance, people with panic disorder have abnormalities in their respiratory system, and are hypersensitive to carbon dioxide inhalation. This, then, might be used as a biomarker of vulnerability to panic attacks. What is more, certain genetic variations seem to influence the short-term outcomes of SSRI treatment.
“Expanding this research area and incorporating these variables in future pharmacological studies may help to unravel significant personalised moderators of outcomes,” says Caldirola.
On top of this, meta-analyses like her own are likely to play an important role – particularly if they include more studies and have a larger base of data to draw from.
“Multicentre collaborations for large prospective studies with standardised designs may provide collection of a large amount of information suitable to be used to make predictions,” she says. “Additionally, recent analytic techniques, such as machine learning, are nowadays available to make reliable predictions from collected data.”
A personalised treatment regimen would not be a cure. However, for those suffering with panic disorders, it could do away with a world of frustration, greatly improving their chances of living a life free from panic attacks.
This article appears in the August 2017 edition of Pharma Technology Focus