Systems biology embedded target validation: improving efficacy in drug discovery

The pharmaceutical industry is faced with a range of challenges with the ever-escalating costs of drug development and a drying out of drug pipelines. By harnessing advances in –omics technologies and moving away from the standard, reductionist model of drug discovery, there is significant potential to reduce costs and improve efficacy. Embedding systems biology approaches in drug discovery, which seek to investigate underlying molecular mechanisms of potential drug targets in a network context, will reduce attrition rates by earlier target validation and the introduction of novel targets into the current stagnant market. Systems biology approaches also have the potential to assist in the design of multidrug treatments and repositioning of existing drugs, while stratifying patients to give a greater personalisation of medical treatment. DISAPPOINTING PROGRESS FOR DRUG DISCOVERY IN THE POST-GENOMIC ERA The standard model of drug discovery is in a bind of increasing costs and risks and decreasing efficiency; regulation is failing to reflect scientific advances while industry productivity flatlines. The average cost for the development of a new drug has been estimated at between $800 million and $2 billion, with high levels of attrition due to lack of efficacy (sidebar 1). Drug discovery was until the late 20 century a straight, phenomenological process: a library of compounds was administered to an animal model, and then potential rescue of disease phenotypes was measured. This approach sought to characterize a disease as a series of symptoms rather than investigating underlying molecular mechanisms. Screening was both laborious and haphazard because drug targets and drug mechanisms of action were in most cases not known. As such we remained ignorant of the molecular effects of these drugs. Even for some of the most common drugs such as aspirin, we still lack knowledge on their full effects. Developments in the fields of signalling networks, robotics, high-throughput arrays and the human genome project have transferred drug discovery away from this phenomenological process towards a more targeted approach. We have moved to a more ‘rational’ targeted screening, in which a specific molecular target is first identified and subsequently a compound is sought that interacts with it. This can happen on a much larger scale than the earlier phenotypic screening. With the successful completion of the human genome project in 2003, there was a hope that researchers would now have the sequence of all genes, and consequently information on all proteins. This vast amount of molecular information would allow for an identification of many more targets, with a concomitant boost to drug discovery. 6 In reality, progress in drug development has been modest: over the last 30 years, an average of 18 new drugs are approved by the US Food and Drug Administration every year, with a more or less steady rate of introduction of drugs with novel targets. Drug attrition remains very high, though this is increasingly due to a lack of efficacy, whereas previously the primary cause was toxicity. This article focuses on why progress in drug development has not lived up to expectations, and how systems biology has the potential to move away from reductionism and deliver significant, costeffective improvements to the drug discovery process. We put forward a concept where systems biology can improve the discovery of new targets based on a network wide understanding of their functions and by affording a more stringent and rational approach to target validation in the early stages of the process. WHY DID DRUG DISCOVERY NOT SKYROCKET? The negligible advances in drug discovery in the genomic age appear to reflect an on-going conservatism in research and development, where conceptual thinking has not kept pace with the new technological capabilities of the –omics sciences that have revolutionised biological discovery. Firstly, focus has not shifted beyond the previously favoured targets, despite the fact that advances in a variety of –omics fields and high-throughput assay development have changed molecular biology into a data-rich science (sidebar 2). We are only using a small segment of the possible targets. The rate of innovation in terms of exploiting drug targets has been stable over the past 30 years, despite the dramatic increase in investment for drug target research. If we consider targets that are encoded by the human genome (excluding for instance virus-encoded targets), less than a quarter of the drugs newly approved each year are targeting novel molecules. In 2011, only 435 of the estimated 30000 genes in the human genome were used as effect-mediating drug targets (with 989 unique approved drugs targeting them). Even more striking is that these targets all belong to a few protein classes. For instance, 36% of these targets are G-protein coupled receptors. Today’s drug discovery efforts in the cancer field are focused on kinases, due to success stories like the Ablkinase inhibitor imatinib mesylate (Gleevec), and reagents targeting receptor tyrosine kinases such as the epidermal growth factor receptor (EGFR) and the vascular endothelial growth factor receptors (VEGFRs). 9 Omics technologies have the potential to point out a less obvious, broader range of drug targets beyond the limited set of protein families that are currently taken into consideration, providing a more holistic view on the biological system. In the human genome for instance, the numbers of protein phosphatases and receptor tyrosine kinases are within the same order of magnitude. This implies that the versatility of functions and specificity of these kinases and phosphatases could also be similar, making phosphatases possible targets of interest. 12 This idea has been put forward before, but the use of systems biology approaches now allows for the consideration of more mechanistic and dynamic detail during target identification, and could therefore reinforce rationales that consider less conservative targets. For instance, proteomics has characterized how a protein phosphatase 4 complex conveys sensitivity to cisplatin, and how phosphatases shape the kinetics of ERK activation and cell fate decisions 14, . While numbers of novel drug targets remain uninspiring, understanding of the underlying molecular functions of the majority of drug targets on cellular or organismal physiology is also surprisingly poor. Put simply, for the majority of possible targets we still do not know what they do in the context of the biomolecular networks they are embedded in, inhibiting a truly rational drug design. Molecular knowledge on drug targets is mostly based on very crude methods such as knock-out mice. These methods generally do not give us detailed molecular information to differentiate effects due to their diverse functions that include different aspects such as scaffolding effects, subcellular compartmentalisation and catalytic activities. There is also a need for a deeper understanding of disease states based on the states of biological networks. SYSTEMS BIOLOGY: CONSIDERING THE COMPLEX BIOLOGICAL NETWORK ENVIRONMENT One important aspect that current reductionist approaches neglect is that drug targets are part of complex dynamic biological networks. The topology and dynamics of these networks effectuates tight control of signalling responses and their resulting biological effects, and these features should be exploited for drug discovery purposes. It is at this point that a systems biology approach has the potential to really improve the drug discovery process, by considering possible targets in the context of their surrounding network. Advances in a variety of –omics fields have massively increased the amount of data we can gather. Systems biology approaches allow for a reduction of the information noise in these large datasets by carving out functional relationships, and for the integration of different types of data into hypothesis-driven mathematical models. Systems biology provides an integrative, multidisciplinary approach that aims to integrate these different types of high-throughput data through knowledge and techniques derived from a diverse range of other disciplines, including engineering, information science and mathematical modelling. This enables a wider signalling network in all its complexity to be considered, instead of adopting the linear, reductionist approaches taken so far. Network based methods are not yet commonly employed in drug development, however they already have proven useful in deciphering molecular mechanisms of diseases, often with very complex traits. A networkbased approach combining interactome, co-expression and co-morbidity data, for instance, was used to predict candidate genes for lipid and lipoprotein traits. Despite the fact that these candidate genes had not been picked up in GWAS studies, the predictions were validated by the identification of novel disease-associated SNPs. Similarly, integrative systems biology approaches combining expression and interaction data into networks have been successful in indicating molecular mechanisms of diseases in different fields, such as the classification of metastatic versus nonmetastatic breast cancer and respiratory diseases , where different public-private partnership consortia have been set up to apply -omics and systems biology techniques to biomarker discovery. Dynamic, mechanistic mathematical models facilitate a holistic perspective on a drug target, revealing the complex environment it exists in, bringing to light new information that would otherwise have remained obscured. Perturbation of a target will have knock-on effects on the rest of the network, and modelling these effects will show which way the network can be best perturbed to achieve the desired effect, and therefore which node in the network could be a suitable target candidate. This could for instance be the catalytic activity of