Drug Repositioning as a Pharmaceutical Strategy: The Obvious Benefits are Real but Beware of Pitfalls That May be Less Apparent.

2021 ◽  
Vol 26 (4) ◽  
Author(s):  
Andrew G. Reaume
Keyword(s):  
Drug Discovery ◽  
Drug Repositioning ◽  
Time Frame ◽  
Lessons Learned ◽  
Advantages And Disadvantages ◽  
A Company

For the last 15 years the drug discovery strategy referred to as “drug-repositioning” has been a recognized approach towards bringing new therapeutics to market and has continued to grow in popularity over this time frame.  Melior Discovery is a company with a founding mission centered on drug repositioning.  The author shares his perspective on lessons learned over 15 years of conducting drug repositioning efforts, complete with advantages and disadvantages that he has encountered using this approach and why, overall, this means of drug discovery provides a compelling business rationale.

Chemogenomic Approaches for Revealing Drug target Interactions in Drug Discovery

2021 ◽  
Vol 22 ◽  
Author(s):  
Harshita Bhargava ◽  
Amita Sharma ◽  
Prashanth Suravajhala
Keyword(s):  
Drug Discovery ◽  
In Silico ◽  
Drug Target ◽  
Drug Repositioning ◽  
Personalised Medicine ◽  
Classification Problem ◽  
Target Interaction ◽  
Advantages And Disadvantages

: The drug discovery process has been a crucial and cost-intensive process. This cost is not only monetary but also involves risks, time, and labour that are incurred while introducing a drug in the market. In order to reduce this cost and the risks associated with the drugs that may result in severe side effects, the in silico methods have gained popularity in recent years. These methods have had a significant impact on not only drug discovery but also the related areas such as drug repositioning, drug-target interaction prediction, drug side effect prediction, personalised medicine, etc. Amongst these research areas predicting interactions between drugs and targets forms the basis for drug discovery. The availability of big data in the form of bioinformatics, genetic databases, along with computational methods, have further supported data-driven decision-making. The results obtained through these methods may be further validated using in vitro or in vivo experiments. This validation step can further justify the predictions resulting from in silico approaches, further increasing the accuracy of the overall result in subsequent stages. A variety of approaches are used in predicting drug-target interactions, including ligand-based, molecular docking based and chemogenomic-based approaches. This paper discusses the chemogenomic methods, considering drug target interaction as a classification problem on whether or not an interaction between a particular drug and target would serve as a basis for understanding drug discovery/drug repositioning. We present the advantages and disadvantages associated with their application.

Recent Advances in Drug Repurposing for Parkinson’s Disease

2019 ◽  
Vol 26 (28) ◽  
pp. 5340-5362 ◽  
Author(s):  
Xin Chen ◽  
Giuseppe Gumina ◽  
Kristopher G. Virga
Keyword(s):  
Parkinson’S Disease ◽  
Parkinson's Disease ◽  
Drug Discovery ◽  
De Novo ◽  
Drug Repositioning ◽  
Drug Repurposing ◽  
Cost Effective ◽  
New Drugs ◽  
Recent Advances ◽  
Clinical Drugs

:As a long-term degenerative disorder of the central nervous system that mostly affects older people, Parkinson’s disease is a growing health threat to our ever-aging population. Despite remarkable advances in our understanding of this disease, all therapeutics currently available only act to improve symptoms but cannot stop the disease progression. Therefore, it is essential that more effective drug discovery methods and approaches are developed, validated, and used for the discovery of disease-modifying treatments for Parkinson’s disease. Drug repurposing, also known as drug repositioning, or the process of finding new uses for existing or abandoned pharmaceuticals, has been recognized as a cost-effective and timeefficient way to develop new drugs, being equally promising as de novo drug discovery in the field of neurodegeneration and, more specifically for Parkinson’s disease. The availability of several established libraries of clinical drugs and fast evolvement in disease biology, genomics and bioinformatics has stimulated the momentums of both in silico and activity-based drug repurposing. With the successful clinical introduction of several repurposed drugs for Parkinson’s disease, drug repurposing has now become a robust alternative approach to the discovery and development of novel drugs for this disease. In this review, recent advances in drug repurposing for Parkinson’s disease will be discussed.

Drug Repositioning: An Approach for Drug Discovery

2016 ◽  
Vol 13 (5) ◽  
pp. 363-376 ◽  
Author(s):  
Parin Vora ◽  
Rakesh Somani ◽  
Madhuri Jain
Keyword(s):  
Drug Discovery ◽  
Drug Repositioning

A project control process in pre‐construction phases

2005 ◽  
Vol 12 (4) ◽  
pp. 351-372 ◽  
Author(s):  
Khaled Al‐Reshaid ◽  
Nabil Kartam ◽  
Narendra Tewari ◽  
Haya Al‐Bader
Keyword(s):  
Construction Projects ◽  
Joint Venture ◽  
Control Process ◽  
Time Frame ◽  
Lessons Learned ◽  
Successful Implementation ◽  
Project Control ◽  
Construction Stage ◽  
Kuwait University ◽  
Content Type

PurposeIt is a well‐known fact that the construction industry always passes through two distinctive problems during the construction stage: slippages of project‐schedules, i.e. time‐frame, and overruns of project‐costs, i.e. budget. However, limited literature is available to solve or dilute these two problems before they even occur. It is strongly believed that the bulk of the two mentioned problems can be mitigated to a great extent, if not eliminated, provided that proper attention is paid to the pre‐construction phases of projects. Normally projects are implemented through traditionally old techniques which generally emphasize only solving “construction problems during the construction phase”. The aim of this article is therefore to unveil a professional methodology known as Project Control System (PCS) focusing on pre‐construction phases of construction projects.Design/methodology/approachIn this article, the authors share the lessons learned during implementation of Kuwait University projects worth approximately $400 million in a span of ten years. The task of the project management/construction management (PM/CM) is being provided to the university by a joint venture team of international and local specialists.FindingsThe pre‐construction methodology ensures smooth and successful implementation during construction phases of the projects as they are generally executed in a fast‐pace, deadline‐driven and cost‐conscious environment. The intuitive proactive methods, if implemented during pre‐construction stage, automatically answer the questions that are encountered during the execution periods of projects.Originality/valueIn this article, the authors share the lessons learned during PM/CM during projects over a span of ten years, which could be of use to others.

scholarly journals High-Throughput Quantitative Assay Technologies for Accelerating the Discovery and Optimization of Targeted Protein Degradation Therapeutics

2021 ◽  
pp. 247255522098504
Author(s):  
Jeffrey R. Simard ◽  
Linda Lee ◽  
Ellen Vieux ◽  
Reina Improgo ◽  
Trang Tieu ◽  
...  
Keyword(s):  
Drug Discovery ◽  
Protein Degradation ◽  
Protein Interactions ◽  
Fluorescent Protein ◽  
De Novo ◽  
Rapid Development ◽  
Dependent Manner ◽  
Western Blots ◽  
Advantages And Disadvantages ◽  
Degradation Rates

The aberrant regulation of protein expression and function can drastically alter cellular physiology and lead to numerous pathophysiological conditions such as cancer, inflammatory diseases, and neurodegeneration. The steady-state expression levels of endogenous proteins are controlled by a balance of de novo synthesis rates and degradation rates. Moreover, the levels of activated proteins in signaling cascades can be further modulated by a variety of posttranslational modifications and protein–protein interactions. The field of targeted protein degradation is an emerging area for drug discovery in which small molecules are used to recruit E3 ubiquitin ligases to catalyze the ubiquitination and subsequent degradation of disease-causing target proteins by the proteasome in both a dose- and time-dependent manner. Traditional approaches for quantifying protein level changes in cells, such as Western blots, are typically low throughput with limited quantification, making it hard to drive the rapid development of therapeutics that induce selective, rapid, and sustained protein degradation. In the last decade, a number of techniques and technologies have emerged that have helped to accelerate targeted protein degradation drug discovery efforts, including the use of fluorescent protein fusions and reporter tags, flow cytometry, time-resolved fluorescence energy transfer (TR-FRET), and split luciferase systems. Here we discuss the advantages and disadvantages associated with these technologies and their application to the development and optimization of degraders as therapeutics.

scholarly journals An integrated dataset for in silico drug discovery

2010 ◽  
Vol 7 (3) ◽  
Author(s):  
Simon J Cockell ◽  
Jochen Weile ◽  
Phillip Lord ◽  
Claire Wipat ◽  
Dmytro Andriychenko ◽  
...  
Keyword(s):  
Systems Biology ◽  
Drug Discovery ◽  
Drug Development ◽  
Data Integration ◽  
In Silico ◽  
Drug Repositioning ◽  
Integrated Systems ◽  
Integration Platform ◽  
Treatment Indications ◽  
New Treatment

SummaryDrug development is expensive and prone to failure. It is potentially much less risky and expensive to reuse a drug developed for one condition for treating a second disease, than it is to develop an entirely new compound. Systematic approaches to drug repositioning are needed to increase throughput and find candidates more reliably. Here we address this need with an integrated systems biology dataset, developed using the Ondex data integration platform, for the in silico discovery of new drug repositioning candidates. We demonstrate that the information in this dataset allows known repositioning examples to be discovered. We also propose a means of automating the search for new treatment indications of existing compounds.

scholarly journals From summative to developmental

2019 ◽  
Vol 27 (3) ◽  
pp. 241-248 ◽  
Author(s):  
Carolyn Steele Gray ◽  
James Shaw
Keyword(s):  
Integrated Care ◽  
Design Thinking ◽  
Complex Interventions ◽  
Service Design ◽  
Lessons Learned ◽  
Portal System ◽  
Contextual Influences ◽  
Content Type ◽  
Advantages And Disadvantages ◽  
Patient Reported

Purpose Models of integrated care are prime examples of complex interventions, incorporating multiple interacting components that work through varying mechanisms to impact numerous outcomes. The purpose of this paper is to explore summative, process and developmental approaches to evaluating complex interventions to determine how to best test this mess. Design/methodology/approach This viewpoint draws on the evaluation and complex intervention literatures to describe the advantages and disadvantages of different methods. The evaluation of the electronic patient reported outcomes (ePRO) mobile application and portal system is presented as an example of how to evaluate complex interventions with critical lessons learned from this ongoing study. Findings Although favored in the literature, summative and process evaluations rest on two problematic assumptions: it is possible to clearly identify stable mechanisms of action; and intervention fidelity can be maximized in order to control for contextual influences. Complex interventions continually adapt to local contexts, making stability and fidelity unlikely. Developmental evaluation, which is more conceptually aligned with service-design thinking, moves beyond these assumptions, emphasizing supportive adaptation to ensure meaningful adoption. Research limitations/implications Blended approaches that incorporate service-design thinking and rely more heavily on developmental strategies are essential for complex interventions. To maximize the benefit of this approach, three guiding principles are suggested: stress pragmatism over stringency; adopt an implementation lens; and use multi-disciplinary teams to run studies. Originality/value This viewpoint offers novel thinking on the debate around appropriate evaluation methodologies to be applied to complex interventions like models of integrated care.

scholarly journals Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

2018 ◽  
Author(s):  
Khader Shameer ◽  
Kipp W. Johnson ◽  
Benjamin S. Glicksberg ◽  
Rachel Hodos ◽  
Ben Readhead ◽  
...  
Keyword(s):  
Machine Learning ◽  
Drug Discovery ◽  
Small Molecule ◽  
Chemical Space ◽  
Drug Repositioning ◽  
Chemical Properties ◽  
Support Vector ◽  
Feature Engineering ◽  
Connectivity Map ◽  
Molecular Features

ABSTRACTDrug repositioning, i.e. identifying new uses for existing drugs and research compounds, is a cost-effective drug discovery strategy that is continuing to grow in popularity. Prioritizing and identifying drugs capable of being repositioned may improve the productivity and success rate of the drug discovery cycle, especially if the drug has already proven to be safe in humans. In previous work, we have shown that drugs that have been successfully repositioned have different chemical properties than those that have not. Hence, there is an opportunity to use machine learning to prioritize drug-like molecules as candidates for future repositioning studies. We have developed a feature engineering and machine learning that leverages data from publicly available drug discovery resources: RepurposeDB and DrugBank. ChemVec is the chemoinformatics-based feature engineering strategy designed to compile molecular features representing the chemical space of all drug molecules in the study. ChemVec was trained through a variety of supervised classification algorithms (Naïve Bayes, Random Forest, Support Vector Machines and an ensemble model combining the three algorithms). Models were created using various combinations of datasets as Connectivity Map based model, DrugBank Approved compounds based model, and DrugBank full set of compounds; of which RandomForest trained using Connectivity Map based data performed the best (AUC=0.674). Briefly, our study represents a novel approach to evaluate a small molecule for drug repositioning opportunity and may further improve discovery of pleiotropic drugs, or those to treat multiple indications.

scholarly journals Comparison Between Internal and External DSLs via RubyTL and Gra2MoL

2012 ◽  
pp. 109-131 ◽  
Author(s):  
Jesús Sánchez Cuadrado ◽  
Javier Luis Cánovas Izquierdo ◽  
Jesús García Molina
Keyword(s):  
Empirical Studies ◽  
Lessons Learned ◽  
The Other ◽  
Model Transformations ◽  
Domain Specific Languages ◽  
Model Driven ◽  
Domain Specific ◽  
Advantages And Disadvantages

Domain Specific Languages (DSL) are becoming increasingly more important with the emergence of Model-Driven paradigms. Most literature on DSLs is focused on describing particular languages, and there is still a lack of works that compare different approaches or carry out empirical studies regarding the construction or usage of DSLs. Several design choices must be made when building a DSL, but one important question is whether the DSL will be external or internal, since this affects the other aspects of the language. This chapter aims to provide developers confronting the internal-external dichotomy with guidance, through a comparison of the RubyTL and Gra2MoL model transformations languages, which have been built as an internal DSL and an external DSL, respectively. Both languages will first be introduced, and certain implementation issues will be discussed. The two languages will then be compared, and the advantages and disadvantages of each approach will be shown. Finally, some of the lessons learned will be presented.

Sign in / Sign up

Export Citation Format

Share Document