Progress in analgesic development: How to assess its real merits?

2021 ◽  
Vol 16 ◽  
Author(s):  
Igor Kissin
Keyword(s):  
Drug Evaluation ◽  
Previous Treatment ◽  
Widespread Application ◽  
Drug Candidates ◽  
Advantages And Disadvantages ◽  
Inflammatory Drugs ◽  
Pain Analgesics ◽  
Approved Drugs ◽  
Multifactorial Approach ◽  
Opioid Receptor Agonists

Background: Assessing analgesic drugs developed over preceding 50 years demonstrated that very intensive efforts directed at diverse molecular pain targets produced thousands of PubMed articles and the introduction of more than 50 new analgesics. Nevertheless, these analgesics did not have a sufficiently broad spectrum of action and level of effectiveness to demonstrably affect the use of opioids or nonsteroidal anti-inflammatory drugs for the treatment of pain. Analgesics in current are only modestly effective in chronic pain (at least with respect to neuropathic pain), and the widespread application of mu opioid receptor agonists for this purpose culminated in the global "opioid crisis”. The introduction of every new drug is regarded as an important success, at least initially. Assessing the merit of a new analgesic is extremely complicated. Objective: The aim of this article is to describe an approach that combines very different categories of drug evaluation – multifactorial approach to assessment of new analgesics. It is based on conclusiveness of clinical trials, novelty of a drug’s molecular target, a drug’s commercial appeal, and the interest in a drug reflected by scientometric indices. Results: This approach was applied to analgesics developed in 1982-2016. It shows that although several new agents have completely novel mechanisms of action, all newly approved drugs, and drug candidates, demonstrated the same persistent problems: relatively low therapeutic advantage over previous treatment and narrow spectrum of use in different types of pain, compared to opioids or NSAIDs. Conclusion: The use of the suggested multifactorial approach to drug assessment may provide a better view of the whole spectrum of analgesics advantages and disadvantages.

A Review of Drug Side Effect Identification Methods

2020 ◽  
Vol 26 (26) ◽  
pp. 3096-3104 ◽  
Author(s):  
Shuai Deng ◽  
Yige Sun ◽  
Tianyi Zhao ◽  
Yang Hu ◽  
Tianyi Zang
Keyword(s):  
Side Effects ◽  
Future Research ◽  
Drug Side Effects ◽  
Important Indicator ◽  
Advantages And Disadvantages ◽  
Key Points ◽  
Network Methods ◽  
Future Research Directions ◽  
The One ◽  
Approved Drugs

Drug side effects have become an important indicator for evaluating the safety of drugs. There are two main factors in the frequent occurrence of drug safety problems; on the one hand, the clinical understanding of drug side effects is insufficient, leading to frequent adverse drug reactions, while on the other hand, due to the long-term period and complexity of clinical trials, side effects of approved drugs on the market cannot be reported in a timely manner. Therefore, many researchers have focused on developing methods to identify drug side effects. In this review, we summarize the methods of identifying drug side effects and common databases in this field. We classified methods of identifying side effects into four categories: biological experimental, machine learning, text mining and network methods. We point out the key points of each kind of method. In addition, we also explain the advantages and disadvantages of each method. Finally, we propose future research directions.

Recent Updates in the Alzheimer’s Disease Etiopathology and Possible Treatment Approaches: A Narrative Review of Current Clinical Trials

2020 ◽  
Vol 13 (4) ◽  
pp. 273-294 ◽  
Author(s):  
Elahe Zarini-Gakiye ◽  
Javad Amini ◽  
Nima Sanadgol ◽  
Gholamhassan Vaezi ◽  
Kazem Parivar
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Clinical Trials ◽  
Small Molecules ◽  
Clinical Trial Design ◽  
Treatment Approaches ◽  
Drug Candidates ◽  
Novel Treatments ◽  
Approved Drugs ◽  
Tau Aggregation

Background: Alzheimer’s disease (AD) is the most frequent subtype of incurable neurodegenerative dementias and its etiopathology is still not clearly elucidated. Objective: Outline the ongoing clinical trials (CTs) in the field of AD, in order to find novel master regulators. Methods: We strictly reviewed all scientific reports from Clinicaltrials.gov and PubMed databases from January 2010 to January 2019. The search terms were “Alzheimer's disease” or “dementia” and “medicine” or “drug” or “treatment” and “clinical trials” and “interventions”. Manuscripts that met the objective of this study were included for further evaluations. Results: Drug candidates have been categorized into two main groups including antibodies, peptides or hormones (such as Ponezumab, Interferon β-1a, Solanezumab, Filgrastim, Levemir, Apidra, and Estrogen), and naturally-derived ingredients or small molecules (such as Paracetamol, Ginkgo, Escitalopram, Simvastatin, Cilostazo, and Ritalin-SR). The majority of natural candidates acted as anti-inflammatory or/and anti-oxidant and antibodies exert their actions via increasing amyloid-beta (Aβ) clearance or decreasing Tau aggregation. Among small molecules, most of them that are present in the last phases act as specific antagonists (Suvorexant, Idalopirdine, Intepirdine, Trazodone, Carvedilol, and Risperidone) or agonists (Dextromethorphan, Resveratrol, Brexpiprazole) and frequently ameliorate cognitive dysfunctions. Conclusion: The presences of a small number of candidates in the last phase suggest that a large number of candidates have had an undesirable side effect or were unable to pass essential eligibility for future phases. Among successful treatment approaches, clearance of Aβ, recovery of cognitive deficits, and control of acute neuroinflammation are widely chosen. It is predicted that some FDA-approved drugs, such as Paracetamol, Risperidone, Escitalopram, Simvastatin, Cilostazoand, and Ritalin-SR, could also be used in off-label ways for AD. This review improves our ability to recognize novel treatments for AD and suggests approaches for the clinical trial design for this devastating disease in the near future.

scholarly journals Thiophene-Based Compounds with Potential Anti-Inflammatory Activity

2021 ◽  
Vol 14 (7) ◽  
pp. 692
Author(s):  
Ryldene Marques Duarte da Cruz ◽  
Francisco Jaime Bezerra Mendonça-Junior ◽  
Natália Barbosa de Mélo ◽  
Luciana Scotti ◽  
Rodrigo Santos Aquino de Araújo ◽  
...  
Keyword(s):  
Inflammatory Diseases ◽  
Structural Characteristics ◽  
Tiaprofenic Acid ◽  
Inflammatory Activity ◽  
Drug Candidates ◽  
Biological Target ◽  
Inflammatory Drugs ◽  
Anti Inflammatory ◽  
Privileged Structures

Rheumatoid arthritis, arthrosis and gout, among other chronic inflammatory diseases are public health problems and represent major therapeutic challenges. Non-steroidal anti-inflammatory drugs (NSAIDs) are the most prescribed clinical treatments, despite their severe side effects and their exclusive action in improving symptoms, without effectively promoting the cure. However, recent advances in the fields of pharmacology, medicinal chemistry, and chemoinformatics have provided valuable information and opportunities for development of new anti-inflammatory drug candidates. For drug design and discovery, thiophene derivatives are privileged structures. Thiophene-based compounds, like the commercial drugs Tinoridine and Tiaprofenic acid, are known for their anti-inflammatory properties. The present review provides an update on the role of thiophene-based derivatives in inflammation. Studies on mechanisms of action, interactions with receptors (especially against cyclooxygenase (COX) and lipoxygenase (LOX)), and structure-activity relationships are also presented and discussed. The results demonstrate the importance of thiophene-based compounds as privileged structures for the design and discovery of novel anti-inflammatory agents. The studies reveal important structural characteristics. The presence of carboxylic acids, esters, amines, and amides, as well as methyl and methoxy groups, has been frequently described, and highlights the importance of these groups for anti-inflammatory activity and biological target recognition, especially for inhibition of COX and LOX enzymes.

scholarly journals High-throughput behavioral screen in C. elegans reveals Parkinson’s disease drug candidates

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Salman Sohrabi ◽  
Danielle E. Mor ◽  
Rachel Kaletsky ◽  
William Keyes ◽  
Coleen T. Murphy
Keyword(s):  
Neural Network ◽  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
High Throughput ◽  
Potential Candidate ◽  
Automated Scoring ◽  
C Elegans ◽  
Drug Candidates ◽  
Branched Chain ◽  
Approved Drugs

AbstractWe recently linked branched-chain amino acid transferase 1 (BCAT1) dysfunction with the movement disorder Parkinson’s disease (PD), and found that RNAi-mediated knockdown of neuronal bcat-1 in C. elegans causes abnormal spasm-like ‘curling’ behavior with age. Here we report the development of a machine learning-based workflow and its application to the discovery of potentially new therapeutics for PD. In addition to simplifying quantification and maintaining a low data overhead, our simple segment-train-quantify platform enables fully automated scoring of image stills upon training of a convolutional neural network. We have trained a highly reliable neural network for the detection and classification of worm postures in order to carry out high-throughput curling analysis without the need for user intervention or post-inspection. In a proof-of-concept screen of 50 FDA-approved drugs, enasidenib, ethosuximide, metformin, and nitisinone were identified as candidates for potential late-in-life intervention in PD. These findings point to the utility of our high-throughput platform for automated scoring of worm postures and in particular, the discovery of potential candidate treatments for PD.

Developing novel chemical entities for the treatment of lysosomal storage disorders: an academic perspective

2015 ◽  
Vol 309 (12) ◽  
pp. F996-F999 ◽  
Author(s):  
James A. Shayman
Keyword(s):  
Drug Targets ◽  
Lysosomal Storage ◽  
Federal Drug Administration ◽  
Drug Candidates ◽  
Gaucher Disease Type 1 ◽  
Biotechnology Companies ◽  
Approved Drugs ◽  
Novel Drug ◽  
Early Drug

Historically, most Federal Drug Administration-approved drugs were the result of “in-house” efforts within large pharmaceutical companies. Over the last two decades, this paradigm has steadily shifted as the drug industry turned to startups, small biotechnology companies, and academia for the identification of novel drug targets and early drug candidates. This strategic pivot has created new opportunities for groups less traditionally associated with the creation of novel therapeutics, including small academic laboratories, for engagement in the drug discovery process. A recent example of the successful development of a drug that had its origins in academia is eliglustat tartrate, an oral agent for Gaucher disease type 1.

scholarly journals PS4DR: A multimodal workflow for identification and prioritization of drugs based on pathway signatures

2020 ◽  
Author(s):  
Mhammad Asif Emon ◽  
Daniel Domingo-Fernández ◽  
Charles Tapley Hoyt ◽  
Martin Hofmann-Apitius
Keyword(s):  
Gene Expression ◽  
Genome Wide Association Study ◽  
Drug Repositioning ◽  
Heterogeneous Data ◽  
New Drugs ◽  
Gwas Data ◽  
Drug Candidates ◽  
Gene Expression Signatures ◽  
Approved Drugs ◽  
Fda Approved Drugs

Abstract Background: During the last decade, there has been a surge towards computational drug repositioning owing to constantly increasing -omics data in the biomedical research field. While numerous existing methods focus on the integration of heterogeneous data to propose candidate drugs, it is still challenging to substantiate their results with mechanistic insights of these candidate drugs. Therefore, there is a need for more innovative and efficient methods which can enable better integration of data and knowledge for drug repositioning. Results: Here, we present a customizable workflow ( PS4DR) which not only integrates high-throughput data such as genome-wide association study (GWAS) data and gene expression signatures from disease and drug perturbations but also takes pathway knowledge into consideration to predict drug candidates for repositioning. We have collected and integrated publicly available GWAS data and gene expression signatures for several diseases and hundreds of FDA-approved drugs or those under clinical trial in this study. Additionally, different pathway databases were used for mechanistic knowledge integration in the workflow. Using this systematic consolidation of data and knowledge, the workflow computes pathway signatures that assist in the prediction of new indications for approved and investigational drugs. Conclusion: We showcase PS4DR with applications demonstrating how this tool can be used for repositioning and identifying new drugs as well as proposing drugs that can simulate disease dysregulations. We were able to validate our workflow by demonstrating its capability to predict FDA-approved drugs for their known indications for several diseases. Further, PS4DR returned many potential drug candidates for repositioning that were backed up by epidemiological evidence extracted from scientific literature. Source code is freely available at https://github.com/ps4dr/ps4dr .

scholarly journals Screening of FDA Approved Drugs Against COVID-19 Main Protease: Coronavirus Disease

2020 ◽  
Author(s):  
Vijayakumar Balakrishnan ◽  
Karthik Lakshminarayanan
Keyword(s):  
Vaccine Development ◽  
New Drugs ◽  
World Health ◽  
Wuhan City ◽  
Drug Candidates ◽  
Human Contact ◽  
Main Protease ◽  
Potent Drug ◽  
Approved Drugs ◽  
Fda Approved Drugs

In the end of December 2019, a new strain of coronavirus was identified in the Wuhan city of Hubei province in China. Within a shorter period of time, an unprecedented outbreak of this strain was witnessed over the entire Wuhan city. This novel coronavirus strain was later officially renamed as COVID-19 (Coronavirus disease 2019) by the World Health Organization. The mode of transmission had been found to be human-to-human contact and hence resulted in a rapid surge across the globe where more than 1,100,000 people have been infected with COVID-19. In the current scenario, finding potent drug candidates for the treatment of COVID-19 has emerged as the most challenging task for clinicians and researchers worldwide. Identification of new drugs and vaccine development may take from a few months to years based on the clinical trial processes. To overcome the several limitations involved in identifying and bringing out potent drug candidates for treating COVID-19, in the present study attempts were made to screen the FDA approved drugs using High Throughput Virtual Screening (HTVS). The COVID-19 main protease (COVID-19 Mpro) was chosen as the drug target for which the FDA approved drugs were initially screened with HTVS. The drug candidates that exhibited favorable docking score, energy and emodel calculations were further taken for performing Induced Fit Docking (IFD) using Schrodinger’s GLIDE. From the flexible docking results, the following four FDA approved drugs Sincalide, Pentagastrin, Ritonavir and Phytonadione were identified. In particular, Sincalide and Pentagastrin can be considered potential key players for the treatment of COVID-19 disease.

Computational Discovery of SARS-CoV-2 NSP 16 Drug Candidates Based on Pharmacophore Modeling and Molecular Dynamics Simulation

2021 ◽  
Vol 20 (04) ◽  
pp. 377-390
Author(s):  
Zahra Hesari ◽  
Samaneh Zolghadri ◽  
Sajad Moradi ◽  
Mohsen Shahlaei ◽  
Elham Tazikeh-Lemeski
Keyword(s):  
Molecular Dynamics ◽  
Study Drug ◽  
Pharmacophore Modeling ◽  
Binding Energies ◽  
Dynamics Simulation ◽  
Structural Protein ◽  
Drug Candidates ◽  
Computational Discovery ◽  
Approved Drugs ◽  
Dynamics Simulations

Non-Structural Protein 16 (NSP-16) is one of the most suitable targets for discovery of drugs for corona viruses including SARS-CoV-2. In this study, drug discovery of SARS-CoV-2 nsp-16 has been accomplished by pharmacophore-based virtual screening among some analogs (FDA approved drugs) and marine natural plants (MNP). The comparison of the binding energies and the inhibition constants was determined using molecular docking method. Three compounds including two FDA approved (Ibrutinib, Idelalisib) and one MNP (Kumusine) were selected for further investigation using the molecular dynamics simulations. The results indicated that Ibrutinib and Idelalisib are oral medications while Kumusine, with proper hydrophilic and solubility properties, is an appropriate candidate for nsp-16 inhibitor and can be effective to control COVID-19 disease.

scholarly journals A Cancer Spheroid Array Chip for Selecting Effective Drug

Micromachines ◽  
2019 ◽  
Vol 10 (10) ◽  
pp. 688 ◽  
Author(s):  
Jae Won Choi ◽  
Sang-Yun Lee ◽  
Dong Woo Lee
Keyword(s):  
Cell Line ◽  
Cell Model ◽  
Drug Evaluation ◽  
Growth Factor Receptor ◽  
Cancer Cell Line ◽  
Lung Cancer Cell Line ◽  
Encapsulated Cells ◽  
Egfr Inhibition ◽  
Drug Candidates ◽  
Cancer Spheroids

A cancer spheroid array chip was developed by modifying a micropillar and microwell structure to improve the evaluation of drugs targeting specific mutations such as phosphor-epidermal growth factor receptor (p-EGFR). The chip encapsulated cells in alginate and allowed cancer cells to grow for over seven days to form cancer spheroids. However, reagents or media used to screen drugs in a high-density spheroid array had to be replaced very carefully, and this was a tedious task. Particularly, the immunostaining of cancer spheroids required numerous steps to replace many of the reagents used for drug evaluation. To solve this problem, we adapted a micropillar and microwell structure to a spheroid array. Thus, culturing cancer spheroids in alginate spots attached to the micropillar allowed us to replace the reagents in the microwell chip with a single fill of fresh medium, without damaging the cancer spheroids. In this study, a cancer spheroid array was made from a p-EGFR-overexpressing cell line (A549 lung cancer cell line). In a 12 by 36 column array chip (25 mm by 75 mm), the spheroid over 100 µm in diameter started to form at day seven and p-EGFR was also considerably overexpressed. The array was used for p-EGFR inhibition and cell viability measurement against seventy drugs, including ten EGFR-targeting drugs. By comparing drug response in the spheroid array (spheroid model) with that in the single-cell model, we demonstrated that the two models showed different responses and that the spheroid model might be more resistant to some drugs, thus narrowing the choice of drug candidates.

Spatial Multi-Criteria Analysis in Environmental Assessment: A Review and Reflection on Benefits and Limitations

2018 ◽  
Vol 20 (03) ◽  
pp. 1840001 ◽  
Author(s):  
Ainhoa Gonzalez ◽  
Álvaro Enríquez-de-Salamanca
Keyword(s):  
Land Use ◽  
Environmental Assessment ◽  
Spatial Data ◽  
Significant Contribution ◽  
Land Use Changes ◽  
Widespread Application ◽  
Advantages And Disadvantages ◽  
Environmental Criteria ◽  
Data Limitations ◽  
Multi Criteria Analysis

Anticipating and avoiding adverse environmental effects resulting from land-use changes and other anthropogenic interventions is the key objective of environmental assessment (EA). EA requires consideration of multiple environmental criteria to establish the receiving environment’s sensitivity and capacity to absorb change. With the increasing availability of and accessibility to spatial data, the adoption of spatial multi-criteria analysis, also known as GIS–MCA, has become a prominent technique to support EA. Using two diverging case studies, this paper reflects upon the advantages and disadvantages of applying GIS–MCA in EA reported in literature. While the significant contribution of this approach to increasing objectivity, transparency and accountability is corroborated, it is recognised that there is no one-fits-all solution. The widespread application of GIS–MCA calls for further research on the effects that methodological assumptions and data limitations may have at various planning hierarchies and decisions, and how these can be addressed to optimise the value of this technique in EA.

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