Elsevier

Drug Discovery Today

Volume 21, Issue 7, July 2016, Pages 1189-1195
Drug Discovery Today

Review
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Drug combination therapy increases successful drug repositioning

https://doi.org/10.1016/j.drudis.2016.05.015Get rights and content

Highlights

  • Publications for drug repositioning have drastically increased in past five years.

  • Different types of drug repositioning and available resources have been summarized.

  • Drug combination increases the success rate of drug repurposing screens.

Repositioning of approved drugs has recently gained new momentum for rapid identification and development of new therapeutics for diseases that lack effective drug treatment. Reported repurposing screens have increased dramatically in number in the past five years. However, many newly identified compounds have low potency; this limits their immediate clinical applications because the known, tolerated plasma drug concentrations are lower than the required therapeutic drug concentrations. Drug combinations of two or more compounds with different mechanisms of action are an alternative approach to increase the success rate of drug repositioning.

Introduction

Although the pharmaceutical industry spends billions of dollars on R&D [1], the number of new drugs approved has been around 40 per year over the past five years (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/DrugInnovation) (Fig. 1a). The success rate of new drug discovery and development does not satisfactorily address the unmet clinical need for disease treatments. Common diseases such as Alzheimer's disease (AD), Parkinson's disease, congestive heart failure and pulmonary hypertension still lack effective therapeutics. In addition, there are over 7800 rare and neglected diseases (https://rarediseases.info.nih.gov), most of which lack approved drug treatments. Although there are 281 approved drugs for these orphan diseases and 600 compounds in clinical trials, there are still approximately 7000 diseases without drug treatment (Fig. 1b). Despite an increase in FDA approvals for drugs for use in rare or orphan diseases in 2014 and 2015 [2] (Fig. 1a), alternative approaches to speed up the drug development for these 7000 diseases are urgently needed.

In the past decade, new technologies such as induced pluripotent stem cells (iPSCs), clustered regularly interspaced short palindromic repeats (CRISPR) gene editing, proteomics and next-generation sequencing have emerged and greatly enhanced research for target identification, disease modeling and drug discovery. Phenotypic screening has regained momentum and has been extensively used in drug discovery and development. However, the translation rate from basic research and drug discovery to approved drugs remains rather disappointing. In the 10-year period from 2006 to 2015, the number of original investigational new drug (IND) applications submitted was stable at around 700 per year (http://www.fda.gov/Drugs/DevelopmentApprovalProcess/) (Fig. 2a); but over this period only 20–40 new drugs were approved each year (Fig. 1a), a less than 6% success rate. Development of new drug therapies remains time-consuming and costly. New strategies, new approaches and new technologies are needed to accelerate new drug discovery and to improve the success rate of drug development. Repositioning existing drugs and drug candidates offers an alternative approach to develop new therapeutics quickly for many diseases that currently do not have treatments.

Section snippets

Different types of drug repurposing

Historically, a number of drugs have been repurposed based on clinical results. Sildenafil (Viagra®) was initially studied for the treatment of hypertension and angina pectoris by Pfizer in the 1980s. It failed for angina, but unexpectedly showed erectile effects. This compound was then marketed as the first oral treatment for erectile dysfunction in the USA [3]. This is an example of drugs that were originally meant to treat one malady but were discovered during clinical trials to have other

Compound collections for drug repurposing screens

As of 31 December 2015, 1539 drugs had been approved by the FDA since its establishment in 1938. Every year another 20–40 new drugs will accumulate in this pool with current trends. In 2015, WHO announced 409 essential medicines [8]. In addition, there is a pool of drug candidates that are either in active clinical trials or have failed in different stages because of insufficient efficacy. Clinical studies registered in the USA as of 14 January 2016 numbered 78,140 (Clinicaltrials.gov.), and

Phenotypic screening assays

Phenotypic screens have a new momentum in drug discovery 9, 10. Different from molecular-target-based ones, phenotypic screens do not require detailed understanding of the disease targets and networks. Phenotypic screens offer the advantage of identifying potential treatments for complicated diseases, where there might be difficulty in identifying the primary therapeutic targets. Executing this approach requires a characteristic phenotype associated with the disease that is known. Cell-based

High IC50 values of identified compounds: a bottleneck in repurposing screens

An emerging challenge for drug repurposing screens is the inability to identify clinically useful compounds for new indications. This could be because of either weak potency of the identified hits, with effective concentration for 50% of the maximum response (IC50) values higher than the safely achievable plasma concentrations in humans, or a simple lack of active compounds. In a malaria repurposing screen [17], 27 of 32 hits identified had high IC50 values (>10 nM) compared with

Drug combination can reduce required drug concentrations of individual drugs

From the literature search and our own recent practice, we have found that drug combination therapy using two-to-three compounds with different mechanisms of action can overcome the above described drug repurposing screen challenge. The use of drugs in combination can produce a synergistic effect if each of the drugs impinges on a different target or signaling pathway that results in reduction of required drug concentrations for each individual drug. Therefore, use of drug combinations could

Polypharmacology: another factor for drug combination therapy

Recently, the importance of multifactor and polygenic pathologies is being recognized for many diseases including neurodegenerative diseases, cancer, diabetes and hypertension. The high attrition rates of drug candidates in clinical trials could partly result from underestimation of the complexity of the pathophysiology in these diseases 22, 23. These diseases might not be caused by a single factor or genetic variant but rather are associated with multiple factors or genetic determinants. In

Computational approach for polypharmacology

Computer-aided drug design is also useful for development of multitargeted drugs or combination therapies. Structure-based methods, ligand-based approaches, QSAR or docking simulation and deep learning are well documented virtual screening technologies 25, 26. The Connectivity map (CMAP) established the first collection for genome-wide transcriptional expression data from small-molecule-treated human cells and simple pattern-matching algorithms [27]. Butte and colleagues performed a large-scale

Existing drug combination therapies

Dysregulation of multiple signaling pathways is a hallmark of cancer 34, 35, 36. Targeting multiple proteins such as kinases in the key pathways might be more effective than a drug targeting a single protein. For example, bosutinib, an approved drug for the treatment of chronic myelogenous leukemia (CML), is an ATP-competitive inhibitor of multiple kinases including the breakpoint cluster region-Abelson fusion protein (Bcr-Abl) tyrosine kinase and Src family kinases (Src, Lyn and Hck) [37].

Potential for drug–drug interaction with drug combination therapy

The potential for adverse drug–drug interactions (DDIs) is a concern when selecting and prioritizing drug combinations with synergistic efficacy for clinical applications. Several types of DDI have been identified and characterized. An adverse DDI could be pharmacodynamic (PD) (target) in origin [45]; however the most common and best characterized adverse DDIs are PK (ADME) in origin. An example of a victim–perpetrator pair is the combination of selective estrogen receptor modulator tamoxifen

Advantages and shortfalls of drug combination therapy for drug repurposing

As we discussed above, there are three major advantages of drug combination therapy for drug repositioning. The first one is the potential for synergistic effects of a drug combination that significantly reduces required drug concentrations for each of the individual drugs used in the combination. This greatly increases the chances for useful clinical applications of such drugs identified from drug repurposing screens, which are otherwise insufficiently active as single agents. The second

Useful tools for drug combination therapy

Computation models are useful tools for predicting drug combinations for potential clinical uses. A comprehensive summary of various bioinformatics approaches and databases for drug repositioning studies has been reviewed [56]. A recent study described an efficient combination drug screening method using feedback system control (FSC) [57]. This method used a phenotypic cell viability assay to generate dose–response curves for each drug first. Then, a differential evolution (DE) algorithm was

Concluding remarks

Drug combination therapy with a synergistic effect can increase the success rates of drug repositioning. A phenotypic repurposing screen allows identification of new therapies from approved drug collections without an understanding of the disease pathophysiology. The identification of effective, synergistic drug combinations could lead to an increased understanding of complicated disease pathophysiology and to the design of better treatments for the disease. Drug repositioning offers hope to

Acknowledgments

This work was supported by the Intramural Research Program of the National Center for Advancing Translational Sciences, National Institutes of Health. The authors would like to thank DeeAnn Visk for reading and critiquing the manuscript.

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