Economic aspects of treating seizure clusters.

Seizure clusters may initiate a chain of events that have economic as well as clinical consequences. The potential economic consequences of seizure clusters must be weighed against the cost of medication to attenuate them. This is true both for individual patients and for society. Data needed for economic analyses include the chance that a cluster will progress to an adverse outcome, such as a need for emergency care, the costs of such an outcome, the cost of a rescue medication (RM), and the effectiveness of the RM. Indirect costs, such as lost employment for patients and caregivers, must also be considered. Several types of economic analyses can be used to determine costs and benefits of a medical intervention. There are studies comparing different RMs from an economic perspective, but there is little direct information on the costs of using an RM versus allowing clusters to run their course. However, the high expense of consequences of seizure clusters makes it likely that effective RMs will make economic as well as medical sense for many patients.

Cost-Based Price Calculation of Mexiletine for Nondystrophic Myotonia.

OBJECTIVES: Mexiletine is a long-known drug used for the treatment of arrhythmias and repurposed in the 1980s for patients with nondystrophic myotonia (NDM). Recently, the price of mexiletine in Europe increased significantly after registration as an orphan drug for NDM. This led to international discussions on affordability and willingness to reimburse mexiletine in the absence of background information that would justify such a price. Our objective was to calculate a cost-based price for mexiletine for adult patients with NDM based on detailed information on development costs. METHODS: We calculated a fair price based on a cost-based pricing model for commercial mexiletine to treat adults with NDM using a recent European drug-pricing model as a framework to include actual costs incurred. Three scenarios were applied: 1 with minimum estimated costs, 1 with maximum estimated costs, and 1 with costs as if mexiletine was innovative. RESULTS: The calculated fair price of mexiletine per patient per year (PPPY) is €452 for the minimum scenario and €1996 for the maximum scenario. By using hypothetical R&D costs used for innovative drugs, the price would be €6685 PPPY. In Europe, the list price of mexiletine ranges from €30 707-60 730 PPPY, based on 600 mg daily. CONCLUSIONS: The current list price for mexiletine in Europe is manifold higher than any scenario of the cost-based models. Accounting for the reduced costs for clinical development in a repurposing scenario, the cost-based pricing model provides a fair commercial price range, which can be used as benchmark for pricing negotiations and/or reimbursement decisions.

Artificial intelligence, machine learning, and drug repurposing in cancer.

Introduction: Drug repurposing provides a cost-effective strategy to re-use approved drugs for new medical indications. Several machine learning (ML) and artificial intelligence (AI) approaches have been developed for systematic identification of drug repurposing leads based on big data resources, hence further accelerating and de-risking the drug development process by computational means.Areas covered: The authors focus on supervised ML and AI methods that make use of publicly available databases and information resources. While most of the example applications are in the field of anticancer drug therapies, the methods and resources reviewed are widely applicable also to other indications including COVID-19 treatment. A particular emphasis is placed on the use of comprehensive target activity profiles that enable a systematic repurposing process by extending the target profile of drugs to include potent off-targets with therapeutic potential for a new indication.Expert opinion: The scarcity of clinical patient data and the current focus on genetic aberrations as primary drug targets may limit the performance of anticancer drug repurposing approaches that rely solely on genomics-based information. Functional testing of cancer patient cells exposed to a large number of targeted therapies and their combinations provides an additional source of repurposing information for tissue-aware AI approaches.

Drug Repositioning: New Approaches and Future Prospects for Life-Debilitating Diseases and the COVID-19 Pandemic Outbreak.

Traditionally, drug discovery utilises a de novo design approach, which requires high cost and many years of drug development before it reaches the market. Novel drug development does not always account for orphan diseases, which have low demand and hence low-profit margins for drug developers. Recently, drug repositioning has gained recognition as an alternative approach that explores new avenues for pre-existing commercially approved or rejected drugs to treat diseases aside from the intended ones. Drug repositioning results in lower overall developmental expenses and risk assessments, as the efficacy and safety of the original drug have already been well accessed and approved by regulatory authorities. The greatest advantage of drug repositioning is that it breathes new life into the novel, rare, orphan, and resistant diseases, such as Cushing's syndrome, HIV infection, and pandemic outbreaks such as COVID-19. Repositioning existing drugs such as Hydroxychloroquine, Remdesivir, Ivermectin and Baricitinib shows good potential for COVID-19 treatment. This can crucially aid in resolving outbreaks in urgent times of need. This review discusses the past success in drug repositioning, the current technological advancement in the field, drug repositioning for personalised medicine and the ongoing research on newly emerging drugs under consideration for the COVID-19 treatment.

Fast Identification of Possible Drug Treatment of Coronavirus Disease-19 (COVID-19) through Computational Drug Repurposing Study.

The recent outbreak of novel coronavirus disease-19 (COVID-19) calls for and welcomes possible treatment strategies using drugs on the market. It is very efficient to apply computer-aided drug design techniques to quickly identify promising drug repurposing candidates, especially after the detailed 3D structures of key viral proteins are resolved. The virus causing COVID-19 is SARS-CoV-2. Taking advantage of a recently released crystal structure of SARS-CoV-2 main protease in complex with a covalently bonded inhibitor, N3 (Liu et al., 10.2210/pdb6LU7/pdb), I conducted virtual docking screening of approved drugs and drug candidates in clinical trials. For the top docking hits, I then performed molecular dynamics simulations followed by binding free energy calculations using an end point method called MM-PBSA-WSAS (molecular mechanics/Poisson-Boltzmann surface area/weighted solvent-accessible surface area; Wang, Chem. Rev. 2019, 119, 9478; Wang, Curr. Comput.-Aided Drug Des. 2006, 2, 287; Wang; ; Hou J. Chem. Inf. Model., 2012, 52, 1199). Several promising known drugs stand out as potential inhibitors of SARS-CoV-2 main protease, including carfilzomib, eravacycline, valrubicin, lopinavir, and elbasvir. Carfilzomib, an approved anticancer drug acting as a proteasome inhibitor, has the best MM-PBSA-WSAS binding free energy, -13.8 kcal/mol. The second-best repurposing drug candidate, eravacycline, is synthetic halogenated tetracycline class antibiotic. Streptomycin, another antibiotic and a charged molecule, also demonstrates some inhibitory effect, even though the predicted binding free energy of the charged form (-3.8 kcal/mol) is not nearly as low as that of the neutral form (-7.9 kcal/mol). One bioactive, PubChem 23727975, has a binding free energy of -12.9 kcal/mol. Detailed receptor-ligand interactions were analyzed and hot spots for the receptor-ligand binding were identified. I found that one hot spot residue, His41, is a conserved residue across many viruses including SARS-CoV, SARS-CoV-2, MERS-CoV, and hepatitis C virus (HCV). The findings of this study can facilitate rational drug design targeting the SARS-CoV-2 main protease.

Drug repositioning: a brief overview.

OBJECTIVES: Drug repositioning, that is, the use of a drug in an indication other than the one for which it was initially marketed, is a growing trend. Its origins lie mainly in the attrition experienced in recent years in the field of new drug discovery. KEY FINDINGS: Despite some regulatory and economic challenges, drug repositioning offers many advantages, and a number of recent successes have confirmed both its public health benefits and its commercial value. The first examples of successful drug repositioning mainly came about through serendipity like acetylsalicylic acid, thalidomide, sildenafil or dimethylfumarate. CONCLUSION: The history of great-repositioned drugs has given some solutions to various pathologies. Serendipity is not yet useful to find repositioning drugs. Drug repositioning is of growing interest. Nowadays, a more rational approach to the identification of drug candidates for repositioning is possible, especially using data mining.

Repurposing Disulfiram as An Anti-Cancer Agent: Updated Review on Literature and Patents.

BACKGROUND: Despite years of success of most anti-cancer drugs, one of the major clinical problems is inherent and acquired resistance to these drugs. Overcoming the drug resistance or developing new drugs would offer promising strategies in cancer treatment. Disulfiram, a drug currently used in the treatment of chronic alcoholism, has been found to have anti-cancer activity. OBJECTIVE: To summarize the anti-cancer effects of Disulfiram through a thorough patent review. METHODS: This article reviews molecular mechanisms and recent patents of Disulfiram in cancer therapy. RESULTS: Several anti-cancer mechanisms of Disulfiram have been proposed, including triggering oxidative stress by the generation of reactive oxygen species, inhibition of the superoxide dismutase activity, suppression of the ubiquitin-proteasome system, and activation of the mitogen-activated protein kinase pathway. In addition, Disulfiram can reverse the resistance to chemotherapeutic drugs by inhibiting the P-glycoprotein multidrug efflux pump and suppressing the activation of NF-kB, both of which play an important role in the development of drug resistance. Furthermore, Disulfiram has been found to reduce angiogenesis because of its metal chelating properties as well as its ability to inactivate Cu/Zn superoxide dismutase and matrix metalloproteinases. Disulfiram has also been shown to inhibit the proteasomes, DNA topoisomerases, DNA methyltransferase, glutathione S-transferase P1, and O6- methylguanine DNA methyltransferase, a DNA repair protein highly expressed in brain tumors. The patents described in this review demonstrate that Disulfiram is useful as an anti-cancer drug. CONCLUSION: For years the FDA-approved, well-tolerated, inexpensive, orally-administered drug Disulfiram was used in the treatment of chronic alcoholism, but it has recently demonstrated anti-cancer effects in a range of solid and hematological malignancies. Its combination with copper at clinically relevant concentrations might overcome the resistance of many anti-cancer drugs in vitro, in vivo, and in patients.

GOF/LOF knowledge inference with tensor decomposition in support of high order link discovery for gene, mutation and disease.

For discovery of new usage of drugs, the function type of their target genes plays an important role, and the hypothesis of "Antagonist-GOF" and "Agonist-LOF" has laid a solid foundation for supporting drug repurposing. In this research, an active gene annotation corpus was used as training data to predict the gain-of-function or loss-of-function or unknown character of each human gene after variation events. Unlike the design of(entity, predicate, entity) triples in a traditional three way tensor, a four way and a five way tensor, GMFD-/GMAFD-tensor, were designed to represent higher order links among or among part of these entities: genes(G), mutations(M), functions(F), diseases( D) and annotation labels(A). A tensor decomposition algorithm, CP decomposition, was applied to the higher order tensor and to unveil the correlation among entities. Meanwhile, a state-of-the-art baseline tensor decomposition algorithm, RESCAL, was carried on the three way tensor as a comparing method. The result showed that CP decomposition on higher order tensor performed better than RESCAL on traditional three way tensor in recovering masked data and making predictions. In addition, The four way tensor was proved to be the best format for our issue. At the end, a case study reproducing two disease-gene-drug links(Myelodysplatic Syndromes-IL2RA-Aldesleukin, Lymphoma- IL2RA-Aldesleukin) presented the feasibility of our prediction model for drug repurposing.

[Biosimilars antibodies: positioning compared to originators - the experience in rheumatology and the biosimilars of trastuzumab in oncology]. / Anticorps biosimilaires versus princeps - L'expérience en rhumatologie et les biosimilaires du trastuzumab en oncologie.

Biosimilars have demonstrated their equivalence with biologic originators, according to rigorous specifications imposed by the regulatory agencies, the FDA and the EMA. Their development is justified by the very high cost of biopharmaceuticals, and strong incentives for their prescription lead us to hope substantial savings, allowing to finance other innovative molecules. Trastuzumab marked history of the treatment of breast cancer. Four biosimilars of trastuzumab are available for routine use and we will detail the key points of their development.

TITLE: Anticorps biosimilaires versus princeps - L'expérience en rhumatologie et les biosimilaires du trastuzumab en oncologie. ABSTRACT: Les biosimilaires sont des produits ayant montré leur équivalence avec le traitement biologique de référence, selon un cahier des charges strict imposé par les agences d'enregistrement, la FDA et l'EMA. Leur développement et leur utilisation sont justifiés par le coût très élevé des biomédicaments, et de fortes incitations à leur prescription font espérer des économies substantielles, permettant le financement d'autres molécules innovantes. Le trastuzumab a marqué l'histoire des traitements du cancer du sein, quatre biosimilaires étant désormais disponibles pour une utilisation en routine. Nous détaillerons ici les points clés de leur développement.

Current state and outlook for drug repositioning anticipated in the field of ovarian cancer.

Ovarian cancer is the seventh most common cancer and the eighth most common cause of cancer mortality in women. Although standard chemotherapy is the established treatment for ovarian cancer, the prognosis remains poor, and it is highly anticipated that new drugs will be developed. New drugs, such as humanized anti-vascular endothelial growth factor monoclonal antibodies and poly ADP-ribose polymerase inhibitors, are expected to improve clinical outcomes of ovarian cancer. However, long-term, costly research is required to develop such new drugs, and soaring national healthcare costs are becoming a concern worldwide. In this social context, drug repositioning, wherein existing drugs are used to develop drugs with new indications for other diseases, has recently gained attention. Because trials have already confirmed the safety in humans and the pharmacokinetics of such drugs, the development period is shorter than the conventional development of a new drug, thereby reducing costs. This review discusses the available basic experimental and clinical data on drugs used for other types of cancer for which drug repositioning is anticipated to repurpose the drug for the treatment of ovarian cancer. These include statins, which are used to treat dyslipidemia; bisphosphonate, which is used to treat osteoporosis; metformin, which is used to treat diabetes; non-steroidal anti-inflammatory drugs; ivermectin, an antiparasitic agent; and itraconazole, an anti-fungal agent. These drugs will play an important role in future drug repositioning strategies for ovarian cancer. Furthermore, drug repositioning is anticipated to extend not only to ovarian cancer treatment but also to ovarian cancer prevention.

eRepo-ORP: Exploring the Opportunity Space to Combat Orphan Diseases with Existing Drugs.

About 7000 rare, or orphan, diseases affect more than 350 million people worldwide. Although these conditions collectively pose significant health care problems, drug companies seldom develop drugs for orphan diseases due to extremely limited individual markets. Consequently, developing new treatments for often life-threatening orphan diseases is primarily contingent on financial incentives from governments, special research grants, and private philanthropy. Computer-aided drug repositioning is a cheaper and faster alternative to traditional drug discovery offering a promising venue for orphan drug research. Here, we present eRepo-ORP, a comprehensive resource constructed by a large-scale repositioning of existing drugs to orphan diseases with a collection of structural bioinformatics tools, including eThread, eFindSite, and eMatchSite. Specifically, a systematic exploration of 320,856 possible links between known drugs in DrugBank and orphan proteins obtained from Orphanet reveals as many as 18,145 candidates for repurposing. In order to illustrate how potential therapeutics for rare diseases can be identified with eRepo-ORP, we discuss the repositioning of a kinase inhibitor for Ras-associated autoimmune leukoproliferative disease. The eRepo-ORP data set is available through the Open Science Framework at https://osf.io/qdjup/.

New Therapeutic Uses for Existing Drugs.

Eighty percent of drugs that enter human clinical testing are never approved for use. This means that for every five drugs that make it into the clinic, there are four that failed to show effectiveness for treating the disease or condition the drug was designed to treat.This high failure rate means there are many existing, partially developed therapeutic candidates with known pharmacology, formulation, and potential toxicity. Finding new uses for existing experimental drugs or biologics "repositioning" builds upon previous research and development efforts, so new candidate therapies can be advanced to clinical trials for a new use more quickly than starting from scratch.Federal funding initiatives in the U.S. and UK started to support pre-clinical /or early stage trials for repositioning existing experimental drugs or biologics (therapies). This chapter covers some of the process issues that have been solved and the remaining challenges that are still in need of solutions. The chapter is primarily written from a U.S. federal funding perspective. The general concepts could be applied more globally to benefit rare and neglected disease populations. The drug development and process bottlenecks are the same for both rare and common disease.