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European Biopharmaceutical Review

Same Drug: New Purpose

Repositioning or repurposing – finding a new indication for an existing drug – has been used to speed up productivity issues in current drug development and address unmet patient needs. A number of drugs have reached the market using this strategy, and have overcome the challenges of exclusivity and off-label use.

Market Strategies

The business models for repurposing marketed drugs – compared to repositioning proprietary shelved assets – are distinct, each presenting their unique opportunities and hurdles, but many of the computational approaches used to identify lead assets are the same. Discovery of a new indication for an old drug can form the basis of a patent, if the innovation is novel, unexpected and potentially clinically beneficial. The claims can also apply to both on-patent and offpatent drugs if the new use has not been previously disclosed or covered in the original patents pertaining to the drug.

In some instances, the period of exclusivity obtained with method of use patents is deemed not commensurable with the investment into a novel drug. However, several strategies can be applied to achieve both market exclusivity and pricing power, including the development of a new formulation in conjunction with the new use claim and/or repurposing drug combinations.

In this sense, drug combinations are doubly appealing, as they can cover multiple pathological pathways of a disease at the same time, yielding an even more powerful effect. This is especially appropriate because many important illnesses have complex causes; in cancer, for example, studies have demonstrated that certain mutations within a targeted molecule, or at another molecule in a different pathway, can render a drug ineffective (1).

Drug repositioning strategies seek to exploit the notion of polypharmacology – the idea that it is common for a drug to interact with multiple protein targets – together with the high connectivity among apparently unrelated cellular processes to identify new therapeutic uses for already approved drugs.

Historically, new indications for existing drugs were discovered by chance. Today, however, technology platforms are aiding pharma companies in developing more targeted and systematic approaches to drug repurposing. The prevalence of drug repositioning studies has resulted in a variety of innovative computational methods for the identification of new opportunities for the use of old drugs.

Signature-Based Approach

The plethora of omics data and new computational approaches have increased the appeal for drug repurposing strategies, as opposed to those de novo, thus allowing for the precise identification of the best candidates and their mechanism of action. The key advantage of repurposed drugs – the shortening of the discovery process by eliminating or greatly reducing components of the trial process – can further be exploited by supplying the companion diagnostics for safety, toxicity, efficacy and patient stratification. Mechanism-based repositioning tactics are also able to fully consider the heterogeneity and complexity of patients, while reducing the inefficiency and toxicity caused by patient variability.

The way in which a biological system responds to a drug is complex; the interaction involves more than just the targets that are engaged by the compound. Omics measurements can capture the entire response as either a pattern or signature. Similarly to drugs, in the context of illness, it is possible to obtain a molecular signature of a disease by analysing tissue from both affected and unaffected individuals. Even if the underlying cause remains unknown, the differences in the gene expression pattern between those afflicted and those that are not can be understood. Computational approaches can then leverage the molecular patterns of the drug/and or disease to draw inferences about therapeutic potential (2).

The use of omics technologies for drug repurposing is an emerging field, and with the advancement of microarray and next-generation sequencing techniques, in addition to the increasing availability of vast volumes of genomics data pertinent for drug repositioning studies, its use will only grow further. There are now a number of highthroughput techniques being used to generate molecular data beyond mRNA expression. Adapting bioinformatics approaches to an integrated analysis of multiple data types – including siRNA, methylation, sequence and proteomic data – holds tremendous potential (3).

Network-based methods and systems biology have been successfully applied to prioritise novel disease-associated genes. Systems biology methods seek to understand not only the individual constituents of a biological system, but also how they function together, by studying their complex interactions.

In recent years, several algorithms have been developed, some focusing on local network properties only, and others taking into account the complete network topology.

Common to all approaches is the understanding that novel diseaseassociated candidates are in close overall proximity to known disease genes. The network can restrict the number of possible conclusions obtained from a high-throughput experiment; by observing the links between two significant proteins, it is possible to suspect that they represent much more than chance associations and instead are listed because of an underlying biological process. Transcriptomics, genomics, metabolomics and proteomics can each provide an independent and unique view of the molecular underpinnings of any given state, sources, individuals and time points.

To best study human biology and make use of the large amount of extant data, a systems approach must be employed that takes into account the multilevel integration of data points. This is particularly relevant, since it allows for the evaluation of every new experiment in the context of all prior knowledge generated by the scientific community, and minimises the influence of outliers (4). Systems biology is able to uncover hidden, meaningful conclusions, where old techniques only provide lists of differentially expressed genes or pathways (5).

Biomarker-Guided Repurposing

Repurposing often involves drugs where the mechanism of action is either fully or partly known. Therefore, clinical trials can take advantage of this knowledge, as well as that from early-phase development/testing and predictive biomarkers. Such biomarkers will usually be mechanistically involved in sensitivity or drug resistance.

Using predictive biomarkers in early drug testing may also increase the therapeutic index by improving the efficacy of the drug in question in the selected biomarker favourable population. At the same time, it can avoid druginduced toxicity in the biomarker unfavourable group, as these patients will not be exposed to the substance.

The concept of biomarkers pinpointing those with a favourable response profile – normally a small subgroup of patients within a large population – is at the heart of personalised medicine. Using a companion molecular diagnostic tool can minimise the size, costs and failure rates of drugs in clinical trials, and this also holds true for many new developments. The outcome for a drug like Avastin may have been very different if such biomarkers had been utilised to ensure that the product reached only the appropriate subpopulation. With biomarkers to hand, chemists would have been better equipped to target only the cohort for which the drug was entirely suited. Future drug development lies with the identification of predictive biomarkers capable of categorising those subsets of patients who will benefit from a given therapy.

Leveraging Data


Biomarker-guided repurposing is already extensively applied within the area of chemotherapeutic drugs for cancer treatment (6). The approval of crizotinib for non-small cell lung cancer (NSCLC) provides excellent evidence towards the linking of these two strategies: crizotinib was repositioned from anaplastic large-cell lymphoma treatment and is accompanied by a diagnostic test to identify the subset of NSCLC patients it is effective for (7).

Signature-based drug repurposing methods are more likely to identify successful candidates compared with blinded search or screening approaches. Key to this process are the recent computational techniques that leverage the vast abundance of omics data to identify non-suspected repositioning candidates with their underlying biological processes, allowing for biomarker-guided repurposing. Network-based approaches are particularly well-suited in this area (8).

References

1. Al-Lazikani B, Banerji U and Workman P, Combinatorial drug therapy for cancer in the post-genomic era, Nature Biotechnology 30(7): pp 679-692, 2012
2. Jin G and Wong ST, Toward better drug repositioning: Prioritizing and integrating existing methods into efficient pipelines, Drug Discovery Today 19(5): pp637- 644, 2014
3. Zhang M, Luo H, Xi Z and Rogaeva E, Drug repositioning for diabetes based on ‘omics’ data mining, PloS One 10(5): e0126082, 2015
4. Costello JC et al, A community effort to assess and improve drug sensitivity prediction algorithms, Nature Biotechnology 32(12): pp1,202-1,212, 2014
5. Berg EL, Systems biology in drug discovery and development, Drug Discovery Today 19(2): pp113-125, 2014
6. Stenvang J et al, Biomarker-guided repurposing of chemotherapeutic drugs for cancer therapy: a novel strategy in drug development, Frontiers in Oncology 3: p313, 2013
7. Shaw AT, Yasothan U and Kirkpatrick P, Crizotinib, Nature Reviews Drug Discovery 10(12): pp897-898, 2011
8. Perera S, Artigas L, Mulet R, Mas JM and Sardón T, Systems biology applied to non-alcoholic fatty liver disease: Treatment selection based on the mechanism of action of nutraceuticals, Nutrafoods (13): pp61-68, 2014


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José Manuel Mas is founder and Chief Operations Officer at Anaxomics. He was previously the EU Head of R&D at RPS, and founder and Chief Technology Officer at Infociencia. José holds a degree in Biochemistry, an MSc in Biotechnology and a PhD in Computer Sciences from the Autonomous University of Barcelona, Spain. He has wide experience in the development of biocomputational tools and artificial intelligence techniques.

Judith Farrés is Head of Collaborative Research at Anaxomics. She graduated in Biochemistry from the Autonomous University of Barcelona, and holds an MS and PhD in Biotechnology. Her background includes six years of postdoctoral research at the Institute of Biotechnology, ETH Zürich, working in the fields of molecular biology and bioengineering. Judith has spent six years at Anaxomics, where she is involved in transferring biological/clinical knowledge to computable descriptions, regulatory issues and project management.
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