ABSTRACT
Introduction: Several effective medications are available for treating panic disorder (PD). However, outcomes are unsatisfactory in a number of patients, suggesting the usefulness of expanding the array of antipanic drugs and improving the quality of response to current recommended treatments.
Areas covered: The authors have performed an updated systematic review of pharmacological studies (phase III onwards) to examine whether advances have been made in the last five years. Only four studies were included. D-cycloserine no longer seemed promising as a cognitive-behavioral therapy (CBT) enhancer. Some preliminary findings concerning the optimization of recommended medications deserved consideration, including: the possibility that SSRIs are more effective than CBT alone in treating panic attacks, combined therapy is preferable when agoraphobia is present, and clonazepam is more potent than paroxetine in decreasing panic relapse.
Expert opinion: Given the lack of novel treatments, expanding a personalized approach to the existing medications seems to be the most feasible strategy to improve pharmacotherapy outcomes regarding PD. Recent technological progress, including wearable devices collecting real-time data, ‘big data’ platforms, and application of machine learning techniques might help make outcome prediction more reliable. Further research on previously promising novel treatments is also recommended.
Article highlights
Although many effective medications exist for treating panic disorder (PD), there is need for improvement.
Unfortunately, in the past 5 years, a very scant number of studies were published and no significant advances were made in the pharmacotherapy of PD.
D-cycloserine no longer seemed promising as a cognitive-behavioral therapy enhancer.
Some preliminary results concerning the use of current medications for PD to optimize the therapeutic outcomes were interesting, but they need confirmation.
Future studies should expand a personalized approach to the current medications for PD, identifying reliable predictors of outcome by the use of clinical/psychophysiological profiles, biomarkers, wearable devices, and machine learning techniques.
Further research on previously promising novel-mechanism-based compounds is also recommended.
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Acknowledgments
The authors thank Enago for their professional language editing assistance.
Declaration of interest
The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties
Reviewer disclosures
One referee declares research support from Forest Laboratories in the past 3 years