Computational methods and tools to predict cytochrome P450 metabolism for drug discovery

In this review, we present important, recent developments in the computational prediction of cytochrome P450 (CYP) metabolism in the context of drug discovery. We discuss in silico models for the various aspects of CYP metabolism prediction, including CYP substrate and inhibitor predictors, site of metabolism predictors (i.e., metabolically labile sites within potential substrates) and metabolite structure predictors. We summarize the different approaches taken by these models, such as rule‐based methods, machine learning, data mining, quantum chemical methods, molecular interaction fields, and docking. We highlight the scope and limitations of each method and discuss future implications for the field of metabolism prediction in drug discovery.     

Current tools for norovirus drug discovery

ABSTRACT

Introduction: Rapid transmission of norovirus often occurs due to its low infectious dosage, high genetic diversity and its short incubation time. The viruses cause acute gastroenteritis and may lead to death. Presently, no effective vaccine or selective drugs accepted by the United States Food and Drug Administration (FDA) are available for the treatment of norovirus. Advances in the development of norovirus replicon cell lines, GII.4-Sydney HuNoV strain human B cells, and murine and gnotobiotic pig norovirus models have facilitated the discovery of effective small molecule inhibitors in vitro and in vivo.

Areas covered: This review gives a brief discussion of the biology and replication of norovirus before highlighting the discovery of anti-norovirus molecules. The article coverage includes: an overview of the current state of norovirus drug discovery, the targeting of the norovirus life cycle, the inhibition of structural and nonstructural proteins of norovirus such as proteases and polymerase, and the blockage of virus entry into host cells. Finally, anti-norovirus drugs in the clinical development stage are described.

Expert opinion: The current approach for the counteraction of norovirus focuses on the inhibition of viral RNA polymerase, norovirus 3C-like protease and the structural proteins VP1 as well as the blockade of norovirus entry. Broad-spectrum anti-norovirus molecules, based on the inhibition of 3C-like protease, have been developed. Other host factors and ways to overcome the development of resistance through mutation are also being examined. A dual approach in targeting viral and host factors may lead to an effective counteraction of norovirus infection. Current successes in developing norovirus replicon harboring cells and norovirus infected human cells, as well as murine norovirus models and other animal models such as piglets have facilitated the discovery of effective drugs and helped our understanding of its mechanism of action.     

Maximizing computational tools for successful drug discovery

Drug discovery is an iterative cycle of identifying promising hits followed by lead optimization via bioisosteric replacements. In the search for compounds affording good bioactivity, equal importance should also be placed on achieving those with favorable pharmacokinetic properties. Thus, the balance and realization of both key properties is an intricate problem that requires great caution. In this editorial, the authors explore the available computational tools in the context of the extant of big data that has borne out via advents of the Omics revolution. As such, the selection of appropriate computational tools for analyzing the vast number of chemical libraries, target proteins and interactomes is the first step toward maximizing the chance for success. However, in order to realize this, it is also necessary to have a solid foundation on the big concepts of drug discovery as well as knowing which tools are available in order to give drug discovery scientists the best opportunity.     

Computational approaches for innovative antiepileptic drug discovery

ABSTRACT

Introduction: Despite the approval of a large number of antiepileptic agents over the past 25 years, there has been no significant improvement in efficacy of treatments, with one third of patients suffering from intractable epilepsy. This scenario has prompted the search for innovative drug discovery solutions. While network pharmacology and explanations of the drug resistance phenomena have been proposed to drive the search for more efficacious therapeutic solutions, such alternative approaches have not fully taken hold within the antiepileptic drug discovery community so far.

Areas covered: Herein, the author discusses the impact that network pharmacology and the current hypotheses of refractory epilepsy and drug repurposing could have if integrated with anti-epileptic computer-aided discovery.

Expert opinion: With many complex diseases, the advancement in the understanding of disorder pathophysiology in addition to the contribution of systems biology have rapidly translated into the discovery of novel drug candidates. However, antiepileptic drug developers have fallen a little behind in this regard, with fewer examples of computer-aided antiepileptic drug design and network-based approximations appearing in scientific literature. New generation single-target agents have so far shown limited success in terms of enhanced efficacy; in contrast, multi-target agents could possibly demonstrate improved safety and efficacy.     

Computational modeling in melanoma for novel drug discovery

ABSTRACT

Introduction: There is a growing body of evidence highlighting the applications of computational modeling in the field of biomedicine. It has recently been applied to the in silico analysis of cancer dynamics. In the era of precision medicine, this analysis may allow the discovery of new molecular targets useful for the design of novel therapies and for overcoming resistance to anticancer drugs. According to its molecular behavior, melanoma represents an interesting tumor model in which computational modeling can be applied. Melanoma is an aggressive tumor of the skin with a poor prognosis for patients with advanced disease as it is resistant to current therapeutic approaches.

Areas covered: This review discusses the basics of computational modeling in melanoma drug discovery and development. Discussion includes the in silico discovery of novel molecular drug targets, the optimization of immunotherapies and personalized medicine trials.

Expert opinion: Mathematical and computational models are gradually being used to help understand biomedical data produced by high-throughput analysis. The use of advanced computer models allowing the simulation of complex biological processes provides hypotheses and supports experimental design. The research in fighting aggressive cancers, such as melanoma, is making great strides. Computational models represent the key component to complement these efforts. Due to the combinatorial complexity of new drug discovery, a systematic approach based only on experimentation is not possible. Computational and mathematical models are necessary for bringing cancer drug discovery into the era of omics, big data and personalized medicine.     

Strategies for bringing drug delivery tools into discovery

The past decade has yielded a significant body of literature discussing approaches for development and discovery collaboration in the pharmaceutical industry. As a result, collaborations between discovery groups and development scientists have increased considerably. The productivity of pharma companies to deliver new drugs to the market, however, has not increased and development costs continue to rise. Inability to predict clinical and toxicological response underlies the high attrition rate of leads at every step of drug development. A partial solution to this high attrition rate could be provided by better preclinical pharmacokinetics measurements that inform PD response based on key pathways that drive disease progression and therapeutic response. A critical link between these key pharmacology, pharmacokinetics and toxicology studies is the formulation. The challenges in pre-clinical formulation development include limited availability of compounds, rapid turn-around requirements and the frequent un-optimized physical properties of the lead compounds. Despite these challenges, this paper illustrates some successes resulting from close collaboration between formulation scientists and discovery teams. This close collaboration has resulted in development of formulations that meet biopharmaceutical needs from early stage preclinical in vivo model development through toxicity testing and development risk assessment of pre-clinical drug candidates.     

Systems biology visualization tools for drug target discovery

Importance of the field: Post-genome drug development has been driven by the need to study biological perturbations at the molecular system level. Systems biology visualization tools can help researchers extract hidden patterns from complex and large Omics data sets, model disease molecular mechanisms, and identify drug targets and drugs with good pharmacological and toxicological profiles. Areas covered in this review: This review covers basic concepts in developing and applying information visualization tools to systems biology. We describe a framework and basic data representation schemes for visual data analysis in systems biology. We review major application areas of these visualization tools within drug discovery by focusing on early-stage drug discovery tasks such as disease biology modeling, target identifications and lead identification. We also show case studies and summarize our experience using visualization tools as lessons to our readers. What the reader will gain: The reader will understand what visualization tools are available for diverse types of systems biology studies in drug discovery and understand how these tools can help advance drug development. Take home message: In spite of the complexity inherent in systems biology, proper use of information visualization tools may reveal emerging properties hidden in the data and enhance chances of success for drug discovery.     

Scientific workflows as productivity tools for drug discovery

Large pharmaceutical companies annually invest tens to hundreds of millions of US dollars in research informatics to support their early drug discovery processes. Traditionally, most of these investments are designed to increase the efficiency of drug discovery. The introduction of do-it-yourself scientific workflow platforms has enabled research informatics organizations to shift their efforts toward scientific innovation, ultimately resulting in a possible increase in return on their investments. Unlike the handling of most scientific data and application integration approaches, researchers apply scientific workflows to in silico experimentation and exploration, leading to scientific discoveries that lie beyond automation and integration. This review highlights some key requirements for scientific workflow environments in the pharmaceutical industry that are necessary for increasing research productivity. Examples of the application of scientific workflows in research and a summary of recent platform advances are also provided.     

Computational tools for discovery and interpretation of expression quantitative trait loci

Expression quantitative trait locus (eQTL) analysis is rapidly moving from a cutting-edge concept in genomics to a mature area of investigation, with important connections to genome-wide association studies for human disease, pharmacogenomics and toxicogenomics. Despite the importance of the topic, many investigators must develop their own code or use tools not specifically suited for eQTL analysis. Convenient computational tools are becoming available, but they are not widely publicized, and investigators who are interested in discovery or eQTL, or in using them to interpret genome-wide association study results may have difficulty navigating the available resources. The purpose of this review is to help investigators find appropriate programs for eQTL analysis and interpretation.     

Biochip platforms as functional genomics tools for drug discovery

Improvements in DNA microarray technology have resulted in the generation of data on a scale that, for the first time, permits detailed scrutiny of the human genome. These data provide the foundation for understanding not only the connections between genes and the purpose of genes in the human genome, but also the molecular basis of genetic defects. These advances have the potential to significantly improve healthcare management by improving disease diagnosis and specifically targeting molecular therapy. Herein, the current state of the technology is reviewed, the commercial platforms used by the biopharmaceutical industry are compared and contrasted, and recent efforts in cross-platform data integration are explored.     

Computational techniques in fragment based drug discovery

Fragment based drug discovery is gaining acceptance as a complement to other more established techniques to identify leads and optimize drug candidates. In this review we illustrate areas where fragment based drug discovery has had an impact and point to some examples that show how fragment based analysis is being applied to new arenas. The traditional uses of computational methods in fragment based for lead discovery and optimization and for risk assessment are briefly summarized. The application of fragment analysis for the definition of bioisosteric replacements are discussed together with techniques to characterize the diversity of chemical libraries based on fragment distribution.     

In vitro display technologies - new tools for drug discovery

Over the past decade, several ligand discovery techniques have been developed that mimic the process of natural evolution. Phage display technology is the most established of these methods and has been applied to numerous technological problems including the discovery of novel drugs. More recently, some new display technologies have emerged which, unlike phage display, operate entirely in vitro and have concomitant advantages. This review describes this new generation of display technologies and indicates how they might fit into the modern drug discovery process.     

Stem cells as screening tools in drug discovery

All physiologic processes operate in a cellular setting. Therefore, drug discoverers need the highest quality cells as they pursue the next generation of safe and effective medicines. Recently, investigators have begun to consider stem cells as a new source of predictive, cell-based assays in drug discovery. Stem cell technology still has hurdles to overcome before these cells are fully accepted as decision-making reagents and amenable to high-throughput screening. However, with global research interest in stem cell biology, significant advances in the application of these cells in drug discovery have been reported. These advances are aligned with three important stages of pharmaceutical research: target discovery and validation, identification of efficacious chemical leads, and drug safety pharmacology. This concise review describes the application of stem cells in these areas of drug discovery with emphasis on molecular screening opportunities.     

Computational chemogenomics approaches to systematic knowledge-based drug discovery

Chemogenomics, the identification of all possible drugs for all possible targets, has recently emerged as a new paradigm in drug discovery in which efficiency in the compound design and optimization process is achieved through the gain and reuse of targeted knowledge. As targeted knowledge resides at the interface between chemistry and biology, computational tools aimed at integrating the chemical and biological spaces play a central role in chemogenomics. This review covers the recent progress made in integrative computational approaches to data annotation and knowledge generation for the systematic knowledge-based design and screening of chemical libraries.     

Computational neuropharmacology: dynamical approaches in drug discovery

Computational approaches that adopt dynamical models are widely accepted in basic and clinical neuroscience research as indispensable tools with which to understand normal and pathological neuronal mechanisms. Although computer-aided techniques have been used in pharmaceutical research (e.g. in structure- and ligand-based drug design), the power of dynamical models has not yet been exploited in drug discovery. We suggest that dynamical system theory and computational neuroscience--integrated with well-established, conventional molecular and electrophysiological methods--offer a broad perspective in drug discovery and in the search for novel targets and strategies for the treatment of neurological and psychiatric diseases.