An Overview of Therapeutic Options in Relapsing-remitting Multiple Sclerosis

Saleem, Sidra; Anwar, Arsalan; Fayyaz, Muniba; Anwer, Fatima; Anwar, Faria

doi:10.7759/cureus.5246

Cited by 16 publications

(20 citation statements)

References 50 publications

(30 reference statements)

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“…The main difference between second line DMTs and immunosuppressant therapy is the selectivity of action. The latter are classical cytotoxic immunosuppressants that act by inhibiting DNA synthesis [38,39], while second line drugs act by suppressing or altering the immune system with more specific mechanisms [40].…”

Section: Plos Onementioning

confidence: 99%

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

et al. 2020

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Multiple Sclerosis (MS) progresses at an unpredictable rate, but predictions on the disease course in each patient would be extremely useful to tailor therapy to the individual needs. We explore different machine learning (ML) approaches to predict whether a patient will shift from the initial Relapsing-Remitting (RR) to the Secondary Progressive (SP) form of the disease, using only "real world" data available in clinical routine. The clinical records of 1624 outpatients (207 in the SP phase) attending the MS service of Sant'Andrea hospital, Rome, Italy, were used. Predictions at 180, 360 or 720 days from the last visit were obtained considering either the data of the last available visit (Visit-Oriented setting), comparing four classical ML methods (Random Forest, Support Vector Machine, K-Nearest Neighbours and AdaBoost) or the whole clinical history of each patient (History-Oriented setting), using a Recurrent Neural Network model, specifically designed for historical data. Missing values were handled by removing either all clinical records presenting at least one missing parameter (Feature-saving approach) or the 3 clinical parameters which contained missing values (Record-saving approach). The performances of the classifiers were rated using common indicators, such as Recall (or Sensitivity) and Precision (or Positive predictive value). In the visit-oriented setting, the Record-saving approach yielded Recall values from 70% to 100%, but low Precision (5% to 10%), which however increased to 50% when considering only predictions for which the model returned a probability above a given "confidence threshold". For the History-oriented setting, both indicators increased as prediction time lengthened, reaching values of 67% (Recall) and 42% (Precision) at 720 days. We show how "real world" data can be effectively used to forecast the evolution of MS, leading to high Recall values and propose innovative approaches to improve Precision towards clinically useful values.

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Section: Plos Onementioning

confidence: 99%

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

et al. 2020

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“…Several oral and infusible disease-modifying therapies (DMTs) have been approved for the treatment of RRMS. The approval was based on efficacy in phase III clinical trials, with beneficial effects on clinical measures (relapse rate and to a variable extent disability progression) and on magnetic resonance imaging metrics of inflammatory activity and lesion load (new or enlarging T2-lesions, gadolinium-enhancing lesions, and to a variable extent also brain atrophy) [6]. Recently, with the approval of ocrelizumab for early PPMS and siponimod (registered in the USA, positive Committee for Human Medicinal Products (CHMP) opinion in the EU) for active SPMS [7], the first treatment options for progressive forms of MS became available (Table 1).…”

Section: Introductionmentioning

confidence: 99%

Effects of disease-modifying therapy on peripheral leukocytes in patients with multiple sclerosis

et al. 2020

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Modern disease-modifying therapies (DMTs) in multiple sclerosis (MS) have variable modes of action and selectively suppress or modulate the immune system. In this review, we summarize the predicted and intended as well as unwanted adverse effects on leukocytes in peripheral blood as a result of treatment with DMTs for MS. We link changes in laboratory tests to the possible therapeutic risks that include secondary autoimmunity, infections, and impaired response to vaccinations. Profound knowledge of the intended effects on leukocyte counts, in particular lymphocytes, explained by the mode of action, and adverse effects which may require additional laboratory and clinical vigilance or even drug discontinuation, is needed when prescribing DMTs to treat patients with MS.

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“…MS can be roughly divided into four subtypes, each with different etiologies and disease courses, but with similar symptoms. The most common subtype of MS, and for which the majority of approved therapies are for, is relapsing-remitting MS (RRMS) (182). There are several therapies for MS currently in clinical trials.…”

Section: Multiple Sclerosismentioning

confidence: 99%

Anti-inflammatory and Neuroprotective Agents in Clinical Trials for CNS Disease and Injury: Where Do We Go From Here?

et al. 2020

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Neurological disorders are major contributors to death and disability worldwide. The pathology of injuries and disease processes includes a cascade of events that often involve molecular and cellular components of the immune system and their interaction with cells and structures within the central nervous system. Because of this, there has been great interest in developing neuroprotective therapeutic approaches that target neuroinflammatory pathways. Several neuroprotective anti-inflammatory agents have been investigated in clinical trials for a variety of neurological diseases and injuries, but to date the results from the great majority of these trials has been disappointing. There nevertheless remains great interest in the development of neuroprotective strategies in this arena. With this in mind, the complement system is being increasingly discussed as an attractive therapeutic target for treating brain injury and neurodegenerative conditions, due to emerging data supporting a pivotal role for complement in promoting multiple downstream activities that promote neuroinflammation and degeneration. As we move forward in testing additional neuroprotective and immune-modulating agents, we believe it will be useful to review past trials and discuss potential factors that may have contributed to failure, which will assist with future agent selection and trial design, including for complement inhibitors. In this context, we also discuss inhibition of the complement system as a potential neuroprotective strategy for neuropathologies of the central nervous system.

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An Overview of Therapeutic Options in Relapsing-remitting Multiple Sclerosis

Cited by 16 publications

References 50 publications

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Considering patient clinical history impacts performance of machine learning models in predicting course of multiple sclerosis

Effects of disease-modifying therapy on peripheral leukocytes in patients with multiple sclerosis

Anti-inflammatory and Neuroprotective Agents in Clinical Trials for CNS Disease and Injury: Where Do We Go From Here?

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