Padsevonil randomized Phase IIa trial in treatment-resistant focal epilepsy: a translational approach

Therapeutic options for patients with treatment-resistant epilepsy represent an important unmet need. Addressing this unmet need was the main factor driving the drug discovery program that led to the synthesis of padsevonil, a first-in-class antiepileptic drug candidate that interacts with two therapeutic targets: synaptic vesicle protein 2 and GABA(A) receptors. Two PET imaging studies were conducted in healthy volunteers to identify optimal padsevonil target occupancy corresponding to levels associated with effective antiseizure activity in rodent models. Optimal padsevonil occupancy associated with non-clinical efficacy was translatable to humans for both molecular targets: high (>90%), sustained synaptic vesicle protein 2A occupancy and 10-15% transient GABA(A) receptor occupancy. Rational dose selection enabled clinical evaluation of padsevonil in a Phase Ha proof-of-concept trial (NCT02495844), with a single-dose arm (400 mg bid). Adults with highly treatment-resistant epilepsy, who were experiencing >= 4 focal seizures/week, and had failed to respond to >= 4 antiepileptic drugs, were randomized to receive placebo or padsevonil as addon to their stable regimen. After a 3-week inpatient double-blind period, all patients received padsevonil during an 8-week outpatient open-label period. The primary endpoint was >= 75% reduction in seizure frequency. Of 55 patients randomized, 50 completed the trial (placebo n-26; padsevonil n = 24). Their median age was 36 years (range 18-60), and they had been living with epilepsy for an average of 25 years. They were experiencing a median of 10 seizures/week and 75% had failed >= 8 antiepileptic drugs. At the end of the inpatient period, 30.8% of patients on padsevonil and 11.1% on placebo were >= 75% responders (odds ratio 4.14; P= 0.067). Reduction in median weekly seizure frequency was 53.7% and 12.5% with padsevonil and placebo, respectively (unadjusted P= 0.026). At the end of the outpatient period, 31.4% were >= 75% responders and reduction in median seizure frequency was 55.2% (all patients). During the inpatient period, 63.0% of patients on placebo and 85.7% on padsevonil reported treatment-emergent adverse events. Overall, 50 (90.9%) patients who received padsevonil reported treatment-emergent adverse events, most frequently somnolence (45.5%), dizziness (43.6%) and headache (25.5%); only one patient discontinued due to a treatment-emergent adverse event. Padsevonil was associated with a favourable safety profile and displayed clinically meaningful efficacy in patients with treatment-resistant epilepsy. The novel translational approach and the innovative proof-of-concept trial design maximized signal detection in a small patient population in a short duration, expediting antiepileptic drug development for the population with the greatest unmet need in epilepsy

A Review of the Effectiveness of Neuroimaging Modalities for the Detection of Traumatic Brain Injury

The incidence of traumatic brain injury (TBI) in the United States was 3.5 million cases in 2009, according to the Centers for Disease Control and Prevention. It is a contributing factor in 30.5% of injury-related deaths among civilians. Additionally, since 2000, more than 260,000 service members were diagnosed with TBI, with the vast majority classified as mild or concussive (76%). The objective assessment of TBI via imaging is a critical research gap, both in the military and civilian communities. In 2011, the Department of Defense (DoD) prepared a congressional report summarizing the effectiveness of seven neuroimaging modalities (computed tomography [CT], magnetic resonance imaging [MRI], transcranial Doppler [TCD], positron emission tomography, single photon emission computed tomography, electrophysiologic techniques [magnetoencephalography and electroencephalography], and functional near-infrared spectroscopy) to assess the spectrum of TBI from concussion to coma. For this report, neuroimaging experts identified the most relevant peer-reviewed publications and assessed the quality of the literature for each of these imaging technique in the clinical and research settings. Although CT, MRI, and TCD were determined to be the most useful modalities in the clinical setting, no single imaging modality proved sufficient for all patients due to the heterogeneity of TBI. All imaging modalities reviewed demonstrated the potential to emerge as part of future clinical care. This paper describes and updates the results of the DoD report and also expands on the use of angiography in patients with TBI

Prevalence Estimates of Amyloid Abnormality Across the Alzheimer Disease Clinical Spectrum.

One characteristic histopathological event in Alzheimer disease (AD) is cerebral amyloid aggregation, which can be detected by biomarkers in cerebrospinal fluid (CSF) and on positron emission tomography (PET) scans. Prevalence estimates of amyloid pathology are important for health care planning and clinical trial design. To estimate the prevalence of amyloid abnormality in persons with normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia and to examine the potential implications of cutoff methods, biomarker modality (CSF or PET), age, sex, APOE genotype, educational level, geographical region, and dementia severity for these estimates. This cross-sectional, individual-participant pooled study included participants from 85 Amyloid Biomarker Study cohorts. Data collection was performed from January 1, 2013, to December 31, 2020. Participants had normal cognition, subjective cognitive decline, mild cognitive impairment, or clinical AD dementia. Normal cognition and subjective cognitive decline were defined by normal scores on cognitive tests, with the presence of cognitive complaints defining subjective cognitive decline. Mild cognitive impairment and clinical AD dementia were diagnosed according to published criteria. Alzheimer disease biomarkers detected on PET or in CSF. Amyloid measurements were dichotomized as normal or abnormal using cohort-provided cutoffs for CSF or PET or by visual reading for PET. Adjusted data-driven cutoffs for abnormal amyloid were calculated using gaussian mixture modeling. Prevalence of amyloid abnormality was estimated according to age, sex, cognitive status, biomarker modality, APOE carrier status, educational level, geographical location, and dementia severity using generalized estimating equations. Among the 19 097 participants (mean [SD] age, 69.1 [9.8] years; 10 148 women [53.1%]) included, 10 139 (53.1%) underwent an amyloid PET scan and 8958 (46.9%) had an amyloid CSF measurement. Using cohort-provided cutoffs, amyloid abnormality prevalences were similar to 2015 estimates for individuals without dementia and were similar across PET- and CSF-based estimates (24%; 95% CI, 21%-28%) in participants with normal cognition, 27% (95% CI, 21%-33%) in participants with subjective cognitive decline, and 51% (95% CI, 46%-56%) in participants with mild cognitive impairment, whereas for clinical AD dementia the estimates were higher for PET than CSF (87% vs 79%; mean difference, 8%; 95% CI, 0%-16%; P = .04). Gaussian mixture modeling-based cutoffs for amyloid measures on PET scans were similar to cohort-provided cutoffs and were not adjusted. Adjusted CSF cutoffs resulted in a 10% higher amyloid abnormality prevalence than PET-based estimates in persons with normal cognition (mean difference, 9%; 95% CI, 3%-15%; P = .004), subjective cognitive decline (9%; 95% CI, 3%-15%; P = .005), and mild cognitive impairment (10%; 95% CI, 3%-17%; P = .004), whereas the estimates were comparable in persons with clinical AD dementia (mean difference, 4%; 95% CI, -2% to 9%; P = .18). This study found that CSF-based estimates using adjusted data-driven cutoffs were up to 10% higher than PET-based estimates in people without dementia, whereas the results were similar among people with dementia. This finding suggests that preclinical and prodromal AD may be more prevalent than previously estimated, which has important implications for clinical trial recruitment strategies and health care planning policies

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation; analyses timings and patterns of tumour evolution; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity; and evaluates a range of more-specialized features of cancer genomes