1
|
Therapies to Restore Consciousness in Patients with Severe Brain Injuries: A Gap Analysis and Future Directions. Neurocrit Care 2021; 35:68-85. [PMID: 34236624 PMCID: PMC8266715 DOI: 10.1007/s12028-021-01227-y] [Citation(s) in RCA: 53] [Impact Index Per Article: 17.7] [Reference Citation Analysis] [Abstract] [Key Words] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2021] [Accepted: 03/04/2021] [Indexed: 02/06/2023]
Abstract
Background/Objective For patients with disorders of consciousness (DoC) and their families, the search for new therapies has been a source of hope and frustration. Almost all clinical trials in patients with DoC have been limited by small sample sizes, lack of placebo groups, and use of heterogeneous outcome measures. As a result, few therapies have strong evidence to support their use; amantadine is the only therapy recommended by current clinical guidelines, specifically for patients with DoC caused by severe traumatic brain injury. To foster and advance development of consciousness-promoting therapies for patients with DoC, the Curing Coma Campaign convened a Coma Science Work Group to perform a gap analysis. Methods We consider five classes of therapies: (1) pharmacologic; (2) electromagnetic; (3) mechanical; (4) sensory; and (5) regenerative. For each class of therapy, we summarize the state of the science, identify gaps in knowledge, and suggest future directions for therapy development. Results Knowledge gaps in all five therapeutic classes can be attributed to the lack of: (1) a unifying conceptual framework for evaluating therapeutic mechanisms of action; (2) large-scale randomized controlled trials; and (3) pharmacodynamic biomarkers that measure subclinical therapeutic effects in early-phase trials. To address these gaps, we propose a precision medicine approach in which clinical trials selectively enroll patients based upon their physiological receptivity to targeted therapies, and therapeutic effects are measured by complementary behavioral, neuroimaging, and electrophysiologic endpoints. Conclusions This personalized approach can be realized through rigorous clinical trial design and international collaboration, both of which will be essential for advancing the development of new therapies and ultimately improving the lives of patients with DoC. Supplementary Information The online version contains supplementary material available at 10.1007/s12028-021-01227-y.
Collapse
|
2
|
Chrétien B, Jourdan JP, Davis A, Fedrizzi S, Bureau R, Sassier M, Rochais C, Alexandre J, Lelong-Boulouard V, Dolladille C, Dallemagne P. Disproportionality analysis in VigiBase as a drug repositioning method for the discovery of potentially useful drugs in Alzheimer's disease. Br J Clin Pharmacol 2020; 87:2830-2837. [PMID: 33274491 DOI: 10.1111/bcp.14690] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/11/2020] [Revised: 11/09/2020] [Accepted: 11/28/2020] [Indexed: 12/12/2022] Open
Abstract
Drug repositioning aims to propose new indications for marketed drugs. Although several methods exist, the utility of pharmacovigilance databases for this purpose is unclear. We conducted a disproportionality analysis in the World Health Organization pharmacovigilance database VigiBase to identify potential anticholinesterase drug candidates for repositioning in Alzheimer's disease (AD). METHODS Disproportionality analysis is a validated method for detecting significant associations between drugs and adverse events (AEs) in pharmacovigilance databases. We applied this approach in VigiBase to establish the safety profile displayed by the anticholinesterase drugs used in AD and searched the database for drugs with similar safety profiles. The detected drugs with potential activity against acetylcholinesterase and butyrylcholinesterases (BuChEs) were then evaluated to confirm their anticholinesterase potential. RESULTS We identified 22 drugs with safety profiles similar to AD medicines. Among these drugs, 4 (clozapine, aripiprazole, sertraline and S-duloxetine) showed a human BuChE inhibition rate of over 70% at 10-5 M. Their human BuChE half maximal inhibitory concentration values were compatible with clinical anticholinesterase action in humans at their normal doses. The most active human BuChE inhibitor in our study was S-duloxetine, with a half maximal inhibitory concentration of 1.2 μM. Combined with its ability to inhibit serotonin (5-HT) reuptake, the use of this drug could represent a novel multitarget directed ligand therapeutic strategy for AD. CONCLUSION We identified 4 drugs with repositioning potential in AD using drug safety profiles derived from a pharmacovigilance database. This method could be useful for future drug repositioning efforts.
Collapse
Affiliation(s)
- Basile Chrétien
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Jean-Pierre Jourdan
- Department of Pharmacy, Caen University Hospital, Caen, F-14000, France.,Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Audrey Davis
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Sophie Fedrizzi
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Ronan Bureau
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Marion Sassier
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France
| | - Christophe Rochais
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| | - Joachim Alexandre
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,Pharmacovigilance Regional Center, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Véronique Lelong-Boulouard
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,INSERM UMR 1075, COMETE-MOBILITES "Vieillissement, Pathologie, Santé", Normandie Univ, UNICAEN, Caen, 14000, France
| | - Charles Dolladille
- Department of Pharmacology, Caen University Hospital, Caen, F-14000, France.,EA4650, Signalisation, électrophysiologie et imagerie des lésions d'ischémie-reperfusion myocardique, Normandie Univ, UNICAEN, Caen, 14000, France
| | - Patrick Dallemagne
- Centre d'Etudes et de Recherche sur le Médicament de Normandie (CERMN), Normandie Univ, UNICAEN, Caen, F-14000, France
| |
Collapse
|
3
|
Portanova J, Murray N, Mower J, Subramanian D, Cohen T. aer2vec: Distributed Representations of Adverse Event Reporting System Data as a Means to Identify Drug/Side-Effect Associations. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2020; 2019:717-726. [PMID: 32308867 PMCID: PMC7153155] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
Adverse event report (AER) data are a key source of signal for post marketing drug surveillance. The standard methodology to analyze AER data applies disproportionality metrics, which estimate the strength of drug/side-effect associations from discrete counts of their occurrence at report level. However, in other domains, improvements in predictive modeling accuracy have been obtained through representation learning, where discrete features are replaced by distributed representations learned from unlabeled data. This paper describes aer2vec, a novel representational approach for AER data in which concept embeddings emerge from neural networks trained to predict drug/side-effect co-occurrence. Trained models are evaluated for their utility in identifying drug/side-effect relationships, with improvements over disproportionality metrics in most cases. In addition, we evaluate the utility of an otherwise-untapped resource in the Food and Drug Administration (FDA) AER system - reporter designations of suspected causality - and find that incorporating this information enhances performance of all models evaluated.
Collapse
|
4
|
Hart KL, Perlis RH, McCoy TH. What do patients learn about psychotropic medications on the web? A natural language processing study. J Affect Disord 2020; 260:366-371. [PMID: 31539672 PMCID: PMC6921244 DOI: 10.1016/j.jad.2019.09.043] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2019] [Revised: 07/15/2019] [Accepted: 09/08/2019] [Indexed: 11/15/2022]
Abstract
BACKGROUND Low rates of medication adherence remain a major challenge across psychiatry. In part, this likely reflects patient concerns about safety and adverse effects, accurate or otherwise. We therefore sought to characterize online information about common psychiatric medications in terms of positive and negative sentiment. METHODS We applied a natural language processing tool to score the sentiment expressed in web search results for 51 psychotropic medications across 3 drug classes (antidepressants, antipsychotics, and mood stabilizers), as a means of seeing if articles referencing these medications were generally positive or generally negative in tone. We compared between medications of the same class, and across medication classes. RESULTS Across 12,733 web search results, significant within-class differences in positive (antidepressants: F(24,2682) = 2.97, p < 0.001; antipsychotics: F(16,4029) = 3.25, p < 0.001; mood stabilizers: F(8,2371) = 6.88, p < 0.001) and negative sentiment (antidepressants: F(24,6282) = 11.17, p < 0.001; antipsychotics: F(16, 4029) = 12.13, p < 0.001; mood stabilizers: F(8, 2371) = 13.28, p < 0.001) were identified. Among these were significantly greater negative sentiment for the antidepressants sertraline, duloxetine, venlafaxine, and paroxetine, and for the antipsychotics, quetiapine and risperidone. Conversely, lithium preparations and valproate exhibited less negative sentiment than other mood stabilizing medications. LIMITATIONS While these results provide a novel means of comparing medications, the present analyses cannot be linked to individual patient consumption of this information, or to its influence on their future clinical interactions. CONCLUSIONS Overall, a subset of psychotropic medications were associated with significantly more negative sentiment. Characterizing these differences may allow clinicians to anticipate patient willingness to initiate or continue medications.
Collapse
Affiliation(s)
- Kamber L Hart
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA
| | - Roy H Perlis
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA
| | - Thomas H McCoy
- Center for Quantitative Health, Massachusetts General Hospital, 185 Cambridge Street, 6th Floor, Boston, MA 02114, USA.
| |
Collapse
|