Immediate and sustained terminal complement inhibition with ravulizumab in patients with anti-aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder.

Objective: To assess the pharmacokinetics and pharmacodynamics of the long-acting terminal complement 5 (C5) inhibitor ravulizumab in adults with anti-aquaporin-4 antibody-positive (AQP4+) neuromyelitis optica spectrum disorder (NMOSD) in the phase 3, open-label CHAMPION-NMOSD trial (NCT04201262). Methods: Patients aged 18 years or older received a weight-based intravenous loading dose of ravulizumab (2,400-3,000 mg) on day 1, followed by weight-based maintenance doses (3,000-3,600 mg) on day 15 and once every 8 weeks thereafter. Pharmacokinetic assessments were maximum observed concentration (Cmax, assessed at the end of the infusion) and concentration at the end of the dosing interval (Ctrough, assessed before dosing) for ravulizumab. Pharmacodynamic assessment was time-matched observed free C5 concentration in serum up to 50 weeks. Results: The pharmacokinetic/pharmacodynamic analysis included 58 patients treated with ravulizumab. Serum ravulizumab concentrations at or above the therapeutic threshold (175 µg/mL) were achieved in all patients after administration of the first dose and maintained for 50 weeks. At week 50, the mean (standard deviation) Cmax (n = 51) and Ctrough (n = 52) were 1,887.6 (411.38) and 764.4 (217.68) µg/mL, respectively. Immediate and complete terminal complement inhibition (free C5 serum concentrations < 0.5 µg/mL) was achieved by the end of the first ravulizumab infusion and sustained throughout the treatment period. No treatment-emergent antibodies to ravulizumab were observed. No impact on ravulizumab pharmacokinetics was seen for age, sex, race, hematocrit, hemoglobin, markers of renal and liver impairment, or medications commonly used by patients with NMOSD. Body weight and BMI were significant covariates of ravulizumab pharmacokinetics. Conclusions: Serum ravulizumab concentrations were maintained above the therapeutic threshold in all patients through 50 weeks of treatment. Ravulizumab achieved immediate and complete terminal complement inhibition that was sustained throughout the treatment period in adults with AQP4+ NMOSD.

Uncovering genetic mimics in multiple sclerosis: A single-center clinical exome sequencing study.

Background: Multiple sclerosis (MS) shares clinical/radiological features with several monogenic diseases that can mimic MS. Objective: We aimed to determine if exome sequencing can identify monogenic diseases in patients diagnosed with MS according to the McDonald criteria thus uncovering them as being misdiagnosed. Methods: We performed whole exome sequencing in a cohort of 278 patients with MS, clinically or radiologically isolated syndrome without cerebrospinal fluid-specific oligoclonal bands (CSF-OCBs) (n = 228), a positive family history of MS (n = 44), or both (n = 6), thereby focusing on individuals potentially more likely to have underlying monogenic conditions mimicking MS. We prioritized 495 genes associated with monogenic diseases sharing features with MS. Results: A disease-causing variant in NOTCH3 was identified in one patient without CSF-OCBs, no spinal lesions, with non-response to immunotherapy, and a family history of dementia, thereby converting the diagnosis to cerebral autosomal-dominant arteriopathy with subcortical infarcts and leukoencephalopathy (CADASIL). Moreover, 18 patients (6.5% of total) carried variants of unclear significance. Conclusion: Monogenic diseases being misdiagnosed as MS seem rare in patients diagnosed with MS according to the McDonald criteria, even in CSF-OCB negative cases. The detected pathogenic NOTCH3 variant emphasizes CADASIL as a rare differential diagnosis and highlights the relevance of genetic testing in selected MS cases with atypical presentations.

LST-AI: A deep learning ensemble for accurate MS lesion segmentation.

Automated segmentation of brain white matter lesions is crucial for both clinical assessment and scientific research in multiple sclerosis (MS). Over a decade ago, we introduced an engineered lesion segmentation tool, LST. While recent lesion segmentation approaches have leveraged artificial intelligence (AI), they often remain proprietary and difficult to adopt. As an open-source tool, we present LST-AI, an advanced deep learning-based extension of LST that consists of an ensemble of three 3D U-Nets. LST-AI explicitly addresses the imbalance between white matter (WM) lesions and non-lesioned WM. It employs a composite loss function incorporating binary cross-entropy and Tversky loss to improve segmentation of the highly heterogeneous MS lesions. We train the network ensemble on 491 MS pairs of T1-weighted and FLAIR images, collected in-house from a 3T MRI scanner, and expert neuroradiologists manually segmented the utilized lesion maps for training. LST-AI also includes a lesion location annotation tool, labeling lesions as periventricular, infratentorial, and juxtacortical according to the 2017 McDonald criteria, and, additionally, as subcortical. We conduct evaluations on 103 test cases consisting of publicly available data using the Anima segmentation validation tools and compare LST-AI with several publicly available lesion segmentation models. Our empirical analysis shows that LST-AI achieves superior performance compared to existing methods. Its Dice and F1 scores exceeded 0.62, outperforming LST, SAMSEG (Sequence Adaptive Multimodal SEGmentation), and the popular nnUNet framework, which all scored below 0.56. Notably, LST-AI demonstrated exceptional performance on the MSSEG-1 challenge dataset, an international WM lesion segmentation challenge, with a Dice score of 0.65 and an F1 score of 0.63-surpassing all other competing models at the time of the challenge. With increasing lesion volume, the lesion detection rate rapidly increased with a detection rate of >75% for lesions with a volume between 10 mm3 and 100 mm3. Given its higher segmentation performance, we recommend that research groups currently using LST transition to LST-AI. To facilitate broad adoption, we are releasing LST-AI as an open-source model, available as a command-line tool, dockerized container, or Python script, enabling diverse applications across multiple platforms.

Preventing long-term disability in CIDP: the role of timely diagnosis and treatment monitoring in a multicenter CIDP cohort.

BACKGROUND: Chronic inflammatory demyelinating polyneuropathy (CIDP) is an inflammatory disease affecting the peripheral nerves and the most frequent autoimmune polyneuropathy. Given the lack of established biomarkers or risk factors for the development of CIDP and patients' treatment response, this research effort seeks to identify potential clinical factors that may influence disease progression and overall treatment efficacy. METHODS: In this multicenter, retrospective analysis, we have screened 197 CIDP patients who presented to the University Hospitals in Düsseldorf, Berlin, Cologne, Essen, Magdeburg and Munich between 2018 and 2022. We utilized the respective hospital information system and examined baseline data with clinical examination, medical letters, laboratory results, antibody status, nerve conduction studies, imaging and biopsy findings. Aside from clinical baseline data, we analyzed treatment outcomes using the Standard of Care (SOC) definition, as well as a comparison of an early (within the first 12 months after manifestation) versus late (more than 12 months after manifestation) onset of therapy. RESULTS: In terms of treatment, most patients received intravenous immunoglobulin (56%) or prednisolone (39%) as their first therapy. Patients who started their initial treatment later experienced a worsening disease course, as reflected by a significant deterioration in their Inflammatory Neuropathy Cause and Treatment (INCAT) leg disability score. SOC-refractory patients had worse clinical outcomes than SOC-responders. Associated factors for SOC-refractory status included the presence of fatigue as a symptom and alcohol dependence. CONCLUSION: Timely diagnosis, prompt initiation of treatment and careful monitoring of treatment response are essential for the prevention of long-term disability in CIDP and suggest a "hit hard and early" treatment paradigm.

Prospective study validating a multidimensional treatment decision score predicting the 24-month outcome in untreated patients with clinically isolated syndrome and early relapsing-remitting multiple sclerosis, the ProVal-MS study.

INTRODUCTION: In Multiple Sclerosis (MS), patients´ characteristics and (bio)markers that reliably predict the individual disease prognosis at disease onset are lacking. Cohort studies allow a close follow-up of MS histories and a thorough phenotyping of patients. Therefore, a multicenter cohort study was initiated to implement a wide spectrum of data and (bio)markers in newly diagnosed patients. METHODS: ProVal-MS (Prospective study to validate a multidimensional decision score that predicts treatment outcome at 24 months in untreated patients with clinically isolated syndrome or early Relapsing-Remitting-MS) is a prospective cohort study in patients with clinically isolated syndrome (CIS) or Relapsing-Remitting (RR)-MS (McDonald 2017 criteria), diagnosed within the last two years, conducted at five academic centers in Southern Germany. The collection of clinical, laboratory, imaging, and paraclinical data as well as biosamples is harmonized across centers. The primary goal is to validate (discrimination and calibration) the previously published DIFUTURE MS-Treatment Decision score (MS-TDS). The score supports clinical decision-making regarding the options of early (within 6 months after study baseline) platform medication (Interferon beta, glatiramer acetate, dimethyl/diroximel fumarate, teriflunomide), or no immediate treatment (> 6 months after baseline) of patients with early RR-MS and CIS by predicting the probability of new or enlarging lesions in cerebral magnetic resonance images (MRIs) between 6 and 24 months. Further objectives are refining the MS-TDS score and providing data to identify new markers reflecting disease course and severity. The project also provides a technical evaluation of the ProVal-MS cohort within the IT-infrastructure of the DIFUTURE consortium (Data Integration for Future Medicine) and assesses the efficacy of the data sharing techniques developed. PERSPECTIVE: Clinical cohorts provide the infrastructure to discover and to validate relevant disease-specific findings. A successful validation of the MS-TDS will add a new clinical decision tool to the armamentarium of practicing MS neurologists from which newly diagnosed MS patients may take advantage. Trial registration ProVal-MS has been registered in the German Clinical Trials Register, `Deutsches Register Klinischer Studien` (DRKS)-ID: DRKS00014034, date of registration: 21 December 2018; https://drks.de/search/en/trial/DRKS00014034.

Cortical Thin Patch Fraction Reflects Disease Burden in MS: The Mosaic Approach.

BACKGROUND AND PURPOSE: GM pathology plays an essential role in MS disability progression, emphasizing the importance of neuroradiologic biomarkers to capture the heterogeneity of cortical disease burden. This study aimed to assess the validity of a patch-wise, individual interpretation of cortical thickness data to identify GM pathology, the "mosaic approach," which was previously suggested as a biomarker for assessing and localizing atrophy. MATERIALS AND METHODS: We investigated the mosaic approach in a cohort of 501 patients with MS with respect to 89 internal and 651 external controls. The resulting metric of the mosaic approach is the so-called thin patch fraction, which is an estimate of overall cortical disease burden per patient. We evaluated the mosaic approach with respect to the following: 1) discrimination between patients with MS and controls, 2) classification between different MS phenotypes, and 3) association with established biomarkers reflecting MS disease burden, using general linear modeling. RESULTS: The thin patch fraction varied significantly between patients with MS and healthy controls and discriminated among MS phenotypes. Furthermore, the thin patch fraction was associated with disease burden, including the Expanded Disability Status Scale, cognitive and fatigue scores, and lesion volume. CONCLUSIONS: This study demonstrates the validity of the mosaic approach as a neuroradiologic biomarker in MS. The output of the mosaic approach, namely the thin patch fraction, is a candidate biomarker for assessing and localizing cortical GM pathology. The mosaic approach can furthermore enhance the development of a personalized cortical MS biomarker, given that the thin patch fraction provides a feature on which artificial intelligence methods can be trained. Most important, we showed the validity of the mosaic approach when referencing data with respect to external control MR imaging repositories.