Pharmacological therapies for early and long-term recovery in disorders of consciousness: current knowledge and promising avenues.

Pharmacological treatment options for disorders of consciousness (DoC), which range from coma to minimally conscious state, remain limited due to the heterogeneity of causes such as traumatic brain injury, stroke, and infections. A lack of rigorous clinical trials has led to off-label use of drugs—including dopaminergic and GABAergic agents, antidepressants, statins, and anticonvulsants—often without clear mechanistic understanding. Robust longitudinal trials are needed, prioritizing early subacute intervention and better-defined outcomes that consider immediate responses and long-term quality of life. A shift toward personalized medicine and unified functional frameworks is advocated, along with increased use of AI tools to predict treatment responses.

Study at a glance

Abstract

Disorders of consciousness (DoC) are characterized by impaired arousal and/or awareness, ranging from coma to unresponsive wakefulness syndrome, minimally conscious state, and cognitive motor dissociation. Pharmacological treatment options remain limited, complicated by the heterogeneity of etiologies, such as traumatic brain injury, stroke, and infections. The lack of rigorous clinical trials has led to off-label use of treatments, often without clear mechanistic understanding, posing challenges for effective patient care. In this perspective, the authors report on key studies concerning the effectiveness of pharmacological interventions, including dopaminergic and GABAergic agents, antidepressants, statins, and anticonvulsants, in promoting recovery of consciousness in DoC. Robust longitudinal clinical trials are needed, with priority given to early subacute phase intervention. Outcomes should be better defined, considering immediate responses to medication while also increasing the emphasis on long-term quality of life. Unified functional and mechanistic frameworks are needed to guide research and foster collaboration. Furthermore, a shift toward personalized medicine would benefit this heterogeneous population. Moving forward, assessing the efficacy of more unconventional or 'paradoxical' pharmacological options in treatment plans will be essential. The authors also expect an increased use of AI tools to identify factors that best predict treatment responses.