Nabiximols discontinuation rate in a large population of patients with multiple sclerosis: a 18-month multicentre study.

INTRODUCTION: Delta-δ-tetrahydrocannabinol and cannabidiol (THC:CBD) oromucosal spray is used as an add-on therapy option for moderate to severe multiple sclerosis (MS) spasticity resistant to other medications. Aims of this study were to provide real-life data on long-term clinical outcomes in a large population of Italian patients treated with THC:CBD and to evaluate predictors of THC:CBD therapy continuation. MATERIALS AND METHODS: This prospective observational multicentre Italian study screened all patients with MS consecutively included in the Agenzia Italiana del Farmaco e-registry at the start of THC:CBD treatment (baseline), after 4 weeks (T1), 12±3 weeks (T2), 24±3 weeks (T3), 48±3 weeks (T4) and 72±3 weeks (T5) from baseline. RESULTS: A total of 1845 patients were recruited from 32 MS Italian centres. At T1, 1502 (81.4%) of patients reached a Numerical Rating Scale (NRS) improvement of ≥20%, with an NRS reduction of 26.9% at T1 and of 34.4% at T5. At T5, 725 patients (48.3% of 1502) discontinued treatment with highest discontinuation rate at T2 and T3. Daily number of puffs was generally stable through the observation period. The multivariate analysis showed that higher NRS scores at baseline (OR 2.28, 95% CI 1.15 to 6.36, p<0.01) and higher differences of NRS between T0 and T1 (OR 2.11, 95% CI 1.08 to 8.26, p<0.05) were associated with an increased probability to continue therapy after 18 months. DISCUSSION: THC:CBD effects were sustained for 18 months with a relatively stable number of puffs per day. About 50% of patients abandoned THC:CBD therapy for loss of efficacy or adverse events.

Effects of THC/CBD oromucosal spray on spasticity-related symptoms in people with multiple sclerosis: results from a retrospective multicenter study.

INTRODUCTION: The approval of 9-δ-tetrahydocannabinol (THC)+cannabidiol (CBD) oromucosal spray (Sativex®) in Italy as an add-on medication for the management of moderate to severe spasticity in multiple sclerosis (MS) has provided a new opportunity for MS patients with drug-resistant spasticity. We aimed to investigate the improvement of MS spasticity-related symptoms in a large cohort of patients with moderate to severe spasticity in daily clinical practice. MATERIALS AND METHODS: MS patients with drug-resistant spasticity were recruited from 30 Italian MS centers. All patients were eligible for THC:CBD treatment according to the approved label: ≥ 18 years of age, at least moderate spasticity (MS spasticity numerical rating scale [NRS] score ≥ 4) and not responding to the common antispastic drugs. Patients were evaluated at baseline (T0) and after 4 weeks of treatment (T1) with the spasticity NRS scale and were also asked about meaningful improvements in 6 key spasticity-related symptoms. RESULTS: Out of 1615 enrolled patients, 1432 reached the end of the first month trial period (T1). Of these, 1010 patients (70.5%) reached a ≥ 20% NRS score reduction compared with baseline (initial responders; IR). We found that 627 (43.8% of 1432) patients showed an improvement in at least one spasticity-related symptom (SRSr group), 543 (86.6%) of them belonging to the IR group and 84 (13.4%) to the spasticity NRS non-responders group. CONCLUSION: Our study confirmed that the therapeutic benefit of cannabinoids may extend beyond spasticity, improving spasticity-related symptoms even in non-NRS responder patients.

Evaluation of machine learning algorithms performance for the prediction of early multiple sclerosis from resting-state FMRI connectivity data.

Machine Learning application on clinical data in order to support diagnosis and prognostic evaluation arouses growing interest in scientific community. However, choice of right algorithm to use was fundamental to perform reliable and robust classification. Our study aimed to explore if different kinds of Machine Learning technique could be effective to support early diagnosis of Multiple Sclerosis and which of them presented best performance in distinguishing Multiple Sclerosis patients from control subjects. We selected following algorithms: Random Forest, Support Vector Machine, Naïve-Bayes, K-nearest-neighbor and Artificial Neural Network. We applied the Independent Component Analysis to resting-state functional-MRI sequence to identify brain networks. We found 15 networks, from which we extracted the mean signals used into classification. We performed feature selection tasks in all algorithms to obtain the most important variables. We showed that best discriminant network between controls and early Multiple Sclerosis, was the sensori-motor I, according to early manifestation of motor/sensorial deficits in Multiple Sclerosis. Moreover, in classification performance, Random Forest and Support Vector Machine showed same 5-fold cross-validation accuracies (85.7%) using only this network, resulting to be best approaches. We believe that these findings could represent encouraging step toward the translation to clinical diagnosis and prognosis.