Applications of computer-aided approaches in the development of hepatitis C antiviral agents

ABSTRACT

Introduction: Hepatitis C virus (HCV) is a global health problem that causes several chronic life-threatening liver diseases. The numbers of people affected by HCV are rising annually. Since 2011, the FDA has approved several anti-HCV drugs; while many other promising HCV drugs are currently in late clinical trials.

Areas covered: This review discusses the applications of different computational approaches in HCV drug design.

Expert opinion: Molecular docking and virtual screening approaches have emerged as a low-cost tool to screen large databases and identify potential small-molecule hits against HCV targets. Ligand-based approaches are useful for filtering-out compounds with rich physicochemical properties to inhibit HCV targets. Molecular dynamics (MD) remains a useful tool in optimizing the ligand-protein complexes and understand the ligand binding modes and drug resistance mechanisms in HCV. Despite their varied roles, the application of in-silico approaches in HCV drug design is still in its infancy. A more mature application should aim at modelling the whole HCV replicon in its active form and help to identify new effective druggable sites within the replicon system. With more technological advancements, the roles of computer-aided methods are only going to increase several folds in the development of next-generation HCV drugs.

KEYWORDS:

• Molecular dynamics
• hepatitis C virus
• drug discovery
• direct acting agents
• computer-aided drug design
• protein flexibility
• QSAR
• binding free energy

Article highlights

• HCV remains one of the single most dreadful diseases that causes the loss of several thousands of human lives every year worldwide.

• Past few years have seen a number of FDA approved directly acting anti-HCV agents reaching the markets. But most of them are only suitable to specific genotype(s) of the virus and also tend to cause a number of side effects. Thus, there is an urgent need to identify a promising, side-effects-free and pan-genotypic drugs to target HCV.

• Several computational approaches have played very significant roles in complementing experiments towards understanding the intricate molecular mechanisms of HCV, including the effects of genetic variations from mutations, and identifying and designing promising anti-HCV agents.

• Approaches such as molecular docking and virtual screening have been particularly extremely successful in offering effective low-cost computational solutions in identifying potential hits to target HCV proteins and prioritize them for much expensive experiments. Applications of these approaches have led to the identification of novel chemotypes against HCV targets, thus opening new doors for HCV drug design.

• MD approaches (both standard and enhanced schemes) have been very efficient in revealing molecular-level insights about the impacts of genetic variations in HCV on the inhibitor binding modes, thus on the inhibition potencies against the targets.

• With the development of more sophisticated computational algorithms and high-performance peta-scale supercomputers, the roles of in silico methods are only going to increase several folds in the development of next-generation HCV drugs.

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Declaration of interest

The authors have no relevant affiliations or financial involvement with any organization or entity with a financial interest in or financial conflict with the subject matter or materials discussed in the manuscript. This includes employment, consultancies, honoraria, stock ownership or options, expert testimony, grants or patents received or pending, or royalties.

Additional information

Funding

The authors are funded by the Alberta Cancer Foundation and via a discovery grant from the Natural Sciences and Engineering Research Council (NSERC) of Canada.