Time to exceed pre-randomization monthly seizure count for perampanel in participants with primary generalized tonic-clonic seizures: A potential clinical end point

Time to prerandomization seizure count design sufficiently assessed the safety and tolerability of perampanel for the treatment of primary generalized tonic-clonic seizures

OBJECTIVE

Static assignment of participants in randomized clinical trials to placebo or ineffective treatment confers risk from continued seizures. An alternative trial design of time to exceed prerandomization monthly seizure count (T-PSC) has replicated the efficacy conclusions of traditionally designed trials, with shorter exposure to placebo and ineffective treatment. Trials aim to evaluate efficacy as well as safety and tolerability; therefore, we evaluated whether this T-PSC design also could replicate the trial's safety and tolerability conclusions.

METHODS

We retrospectively applied the T-PSC design to analyze treatment-emergent adverse events (TEAEs) from a blinded, placebo-controlled trial of perampanel for primary generalized tonic-clonic seizures (NCT01393743). The safety analysis set consisted of 81 and 82 participants randomized to perampanel and placebo arms, respectively. We evaluated the incidences of TEAEs, treatment-related TEAEs, serious TEAEs, and TEAEs of special interest that occurred before T-PSC relative to those observed during the full-length trial.

RESULTS

Of the 67 and 59 participants who experienced TEAEs in the perampanel and placebo arms during full-length trial, 66 (99%) and 54 (92%) participants experienced TEAEs with onset occurring before T-PSC, respectively. When limited to treatment-related TEAEs, 55 of 56 (98%) and 32 of 37 (86%) participants reported treatment-related TEAEs that occurred before T-PSC in the perampanel and placebo arms, respectively. There were more TEAEs after T-PSC with placebo as compared to perampanel (Fisher exact odds ratio = 8.6, p = .035), which resulted in overestimation of the difference in TEAE rate. There was a numerical reduction in serious TEAEs (3/13 occurred after T-PSC, one in placebo and two in perampanel).

SIGNIFICANCE

Almost all TEAEs occurred before T-PSC. More treatment-related TEAEs occurred after T-PSC for participants randomized to placebo than perampanel, which may be due to either a shorter T-PSC or delayed time to TEAE for placebo.

Challenges and directions in epilepsy diagnostics and therapeutics: Proceedings of the 17th Epilepsy Therapies and Diagnostics Development conference

Substantial efforts are underway toward optimizing the diagnosis, monitoring, and treatment of seizures and epilepsy. We describe preclinical programs in place for screening investigational therapeutic candidates in animal models, with particular attention to identifying and eliminating drugs that might paradoxically aggravate seizure burden. After preclinical development, we discuss challenges and solutions in the design and regulatory logistics of clinical trial execution, and efforts to develop disease biomarkers and interventions that may be not only seizure-suppressing, but also disease-modifying. As disease-modifying treatments are designed, there is clear recognition that, although seizures represent one critical therapeutic target, targeting nonseizure outcomes like cognitive development or functional outcomes requires changes to traditional designs. This reflects our increasing understanding that epilepsy is a disease with profound impact on quality of life for the patient and caregivers due to both seizures themselves and other nonseizure factors. This review examines selected key challenges and future directions in epilepsy diagnostics and therapeutics, from drug discovery to translational application.

Machine Learning and Artificial Intelligence Applications to Epilepsy: a Review for the Practicing Epileptologist

PURPOSE OF REVIEW

Machine Learning (ML) and Artificial Intelligence (AI) are data-driven techniques to translate raw data into applicable and interpretable insights that can assist in clinical decision making. Some of these tools have extremely promising initial results, earning both great excitement and creating hype. This non-technical article reviews recent developments in ML/AI in epilepsy to assist the current practicing epileptologist in understanding both the benefits and limitations of integrating ML/AI tools into their clinical practice.

RECENT FINDINGS

ML/AI tools have been developed to assist clinicians in almost every clinical decision including (1) predicting future epilepsy in people at risk, (2) detecting and monitoring for seizures, (3) differentiating epilepsy from mimics, (4) using data to improve neuroanatomic localization and lateralization, and (5) tracking and predicting response to medical and surgical treatments. We also discuss practical, ethical, and equity considerations in the development and application of ML/AI tools including chatbots based on Large Language Models (e.g., ChatGPT). ML/AI tools will change how clinical medicine is practiced, but, with rare exceptions, the transferability to other centers, effectiveness, and safety of these approaches have not yet been established rigorously. In the future, ML/AI will not replace epileptologists, but epileptologists with ML/AI will replace epileptologists without ML/AI.

Sustainability of seizure reduction and seizure control with adjunctive cenobamate: Post hoc analysis of a phase 3, open-label study

OBJECTIVE

In this post hoc analysis of a subset of patients from a long-term, open-label phase 3 study, we assessed ≥50%, ≥75%, ≥90%, and 100% seizure reduction and sustainability of these responses with cenobamate using a time-to-event analytical approach.

METHODS

Of 240 patients with uncontrolled focal seizures who had adequate seizure data available, 214 completed the 12-week titration phase and received ≥1 dose of cenobamate in the maintenance phase (max dose 400 mg/day) and were included in this post hoc analysis. Among patients who met an initial given seizure-reduction level (≥50%, ≥75%, ≥90%, or 100%), sustainability of that response was measured using a time-to-event methodology. An event was defined as the occurrence of a study visit at which the seizure frequency during the interval since the prior study visit exceeded the initially attained reduction level. Study visits during the maintenance phase occurred at 3-month intervals.

RESULTS

Of the 214 patients analyzed, 188 (88%), 177 (83%), 160 (75%), and 145 (68%) met ≥50%, ≥75%, ≥90%, and 100% seizure-reduction responses, respectively, for at least one study visit interval during the maintenance phase. The median (95% confidence interval [CI]) time to first visit without a ≥50% seizure reduction was not reached by 30 months of follow-up (53% of patients maintained their initial ≥50% seizure reduction). Median (95% CI) time to first visit without sustaining the initial ≥75%, ≥90%, or 100% seizure reduction was 13.0 (7.5-21.9) months, 7.5 (5.4-11.6) months, and 7.0 (5.3-10.4) months, respectively. Among the 145 patients who had 100% seizure reduction during at least one study visit, 22% remained seizure-free for at least 30 months and 63% had ≤3 study visits with seizures.

SIGNIFICANCE

Adjunctive treatment with cenobamate led to sustained seizure reductions during the maintenance phase of the phase 3 safety study.

Time-to-event clinical trial designs: Existing evidence and remaining concerns

Well-designed placebo-controlled clinical trials are critical to the development of novel treatments for epilepsy, but their design has not changed for decades. Patients, clinicians, regulators, and innovators all have concerns that recruiting for trials is challenging, in part, due to the static design of maintaining participants for long periods on add-on placebo when there are an increasing number of options for therapy. A traditional trial maintains participants on blinded treatment for a static period (e.g., 12 weeks of maintenance), during which participants on placebo have an elevated risk of sudden unexpected death in epilepsy compared to patients on an active treatment. Time-to-event trials observe participants on blinded treatment until a key event occurs (e.g., post-randomization seizure count matches pre-randomization monthly seizure count). In this article, we review the evidence for these designs based on re-analysis of prior trials, one published trial that used a time-to-second seizure design, and experience from an ongoing blinded trial. We also discuss remaining concerns regarding time-to-event trials. We conclude that, despite potential limitations, time-to-event trials are a potential promising mechanism to make trials more patient friendly and reduce placebo exposure, which are urgent needs to improve safety and increase recruitment to trials.