Efficacy and safety clinical trial with efavirenz in patients diagnosed with adult Niemann-pick type C with cognitive impairment.

BACKGROUND: Niemann-Pick disease Type C (NPC) is a genetic, incurable, neurodegenerative disorder. This orphan disease is most frequently caused by mutations in the NPC1 protein, resulting in intralysossomal cholesterol accumulation. NPC1 is found in neuronal cell bodies, axon terminals and synaptosomes, suggesting it plays a role in lysosomal degradation pathway and in synaptic transmission. Neuronal function is especially vulnerable to NPC1 deficiency and synaptic changes seem a key element in disease development. Currently, Miglustat (Zavesca®) is the only approved treatment for NPC. However, preclinical evidence showed that low-dose Efavirenz reverted synaptic defects through pharmacological activation of the enzyme CYP46. METHODS: This is a single-center, phase II clinical trial to evaluate the efficacy and safety of Efavirenz in addition to standard of care in patients diagnosed with adult or late juvenile-onset NPC with cognitive impairment. All enrolled patients will be treated orally with 25 mg/d of Efavirenz for 52 weeks (1 year). Secondary objectives include evaluating clinical (neurological and neuropsychological questionnaires) and biological (imaging and biochemical biomarkers) parameters. DISCUSSION: NPC is still an unmet medical need. Although different therapeutic approaches are under study, this is the first clinical trial (to the best of our knowledge) studying the effects of Efavirenz in adult- and late-juvenile-onset NPC. Despite the small sample size and the single-arm design, we expect the results to show Efavirenz's capacity of activating the CYP46 enzyme to compensate for NPC1 deficiency and correct synaptic changes, therefore compensating cognitive and psychiatric changes in these patients. This study may provide direct benefit to enrolled patients in terms of slowing down the disease progression.

Evaluation of Dysphagia in Motor Neuron Disease. Review of Available Diagnostic Tools and New Perspectives.

Oropharyngeal dysphagia (OD) is highly prevalent (up to 80%) in patients with motor neuron disease (MND), influencing the prognosis of the disease. The clinical assessment of dysphagia is complex. There are assessment scales and screening questionnaires, but they have not been tested in patients with MND. In a sample of 46 patients with MND, the sensitivity and specificity of the EAT-10 and SwalQoL questionnaires, as well as the ALS-SS and FOIS scales, were tested and compared to the gold standard technique (videofluoroscopy, VFS). The patients were stratified using the DOSSc variable according to the video fluoroscopic examination with (n = 37) or without (n = 8) signs of dysphagia, and the results were compared with the scores obtained in the dysphagia questionnaires. None of the studied questionnaires was more sensitive than the others, but one stood out for its high specificity (= 1): the SwalQoL revised FS. The symptom frequency section of the SwalQoL questionnaire with some modifications, (SwalQoL revised FS) may be a useful tool in the clinical assessment of dysphagia because it's capable to detect the patients that really don't have dysphagia. The ALS-SS showed the greatest validity as a severity scale of dysphagia among the sample studied. A specific questionnaire for screening for dysphagia in MND needs to be developed. Until that time, the proposal is to use a combination of the existing questionnaires for other pathologies (EAT-10 and SwalQoL) and the specific scale for MND, the ALS-SS, to make an accurately clinical assessment of OD in MND patients before to perform a videofluoroscopy.

Phantom, clinical, and texture indices evaluation and optimization of a penalized-likelihood image reconstruction method (Q.Clear) on a BGO PET/CT scanner.

INTRODUCTION: The aim of this study was to evaluate the behavior of a penalized-likelihood image reconstruction method (Q.Clear) under different count statistics and lesion-to-background ratios (LBR) on a BGO scanner, in order to obtain an optimum penalization factor (ß value) to study and optimize for different acquisition protocols and clinical goals. METHODS: Both phantom and patient images were evaluated. Data from an image quality phantom were acquired using different Lesion-to-Background ratios and acquisition times. Then, each series of the phantom was reconstructed using ß values between 50 and 500, at intervals of 50. Hot and cold contrasts were obtained, as well as background variability and contrast-to-noise ratio (CNR). Fifteen 18 F-FDG patients (five brain scans and 10 torso acquisitions) were acquired and reconstructed using the same ß values as in the phantom reconstructions. From each lesion in the torso acquisition, noise, contrast, and signal-to-noise ratio (SNR) were computed. Image quality was assessed by two different nuclear medicine physicians. Additionally, the behaviors of 12 different textural indices were studied over 20 different lesions. RESULTS: Q.Clear quantification and optimization in patient studies depends on the activity concentration as well as on the lesion size. In the studied range, an increase on ß is translated in a decrease in lesion contrast and noise. The net product is an overall increase in the SNR, presenting a tendency to a steady value similar to the CNR in phantom data. As the activity concentration or the sphere size increase the optimal ß increases, similar results are obtained from clinical data. From the subjective quality assessment, the optimal ß value for torso scans is in a range between 300 and 400, and from 100 to 200 for brain scans. For the recommended torso ß values, texture indices present coefficients of variation below 10%. CONCLUSIONS: Our phantom and patients demonstrate that improvement of CNR and SNR of Q.Clear algorithm which depends on the studied conditions and the penalization factor. Using the Q.Clear reconstruction algorithm in a BGO scanner, a ß value of 350 and 200 appears to be the optimal value for 18F-FDG oncology and brain PET/CT, respectively.