The innovative medicines initiative: a public private partnership model to foster drug discovery

Sharing Medical Big Data While Preserving Patient Confidentiality in Innovative Medicines Initiative: A Summary and Case Report from BigData@Heart

Sharing individual patient data (IPD) is a simple concept but complex to achieve due to data privacy and data security concerns, underdeveloped guidelines, and legal barriers. Sharing IPD is additionally difficult in big data-driven collaborations such as Bigdata@Heart in the Innovative Medicines Initiative, due to competing interests between diverse consortium members. One project within BigData@Heart, case study 1, needed to pool data from seven heterogeneous data sets: five randomized controlled trials from three different industry partners, and two disease registries. Sharing IPD was not considered feasible due to legal requirements and the sensitive medical nature of these data. In addition, harmonizing the data sets for a federated data analysis was difficult due to capacity constraints and the heterogeneity of the data sets. An alternative option was to share summary statistics through contingency tables. Here it is demonstrated that this method along with anonymization methods to ensure patient anonymity had minimal loss of information. Although sharing IPD should continue to be encouraged and strived for, our approach achieved a good balance between data transparency while protecting patient privacy. It also allowed a successful collaboration between industry and academia.

Predictive validity in drug discovery: what it is, why it matters and how to improve it

Successful drug discovery is like finding oases of safety and efficacy in chemical and biological deserts. Screens in disease models, and other decision tools used in drug research and development (R&D), point towards oases when they score therapeutic candidates in a way that correlates with clinical utility in humans. Otherwise, they probably lead in the wrong direction. This line of thought can be quantified by using decision theory, in which 'predictive validity' is the correlation coefficient between the output of a decision tool and clinical utility across therapeutic candidates. Analyses based on this approach reveal that the detectability of good candidates is extremely sensitive to predictive validity, because the deserts are big and oases small. Both history and decision theory suggest that predictive validity is under-managed in drug R&D, not least because it is so hard to measure before projects succeed or fail later in the process. This article explains the influence of predictive validity on R&D productivity and discusses methods to evaluate and improve it, with the aim of supporting the application of more effective decision tools and catalysing investment in their creation.

On drug discovery against infectious diseases and academic medicinal chemistry contributions

This perspective is an attempt to document the problems that medicinal chemists are facing in drug discovery. It is also trying to identify relevant/possible, research areas in which academics can have an impact and should thus be the subject of grant calls. Accordingly, it describes how hit discovery happens, how compounds to be screened are selected from available chemicals and the possible reasons for the recurrent paucity of useful/exploitable results reported. This is followed by the successful hit to lead stories leading to recent and original antibacterials which are, or about to be, used in human medicine. Then, illustrated considerations and suggestions are made on the possible inputs of academic medicinal chemists. This starts with the observation that discovering a “good” hit in the course of a screening campaign still rely on a lot of luck – which is within the reach of academics –, that the hit to lead process requires a lot of chemistry and that if public–private partnerships can be important throughout these stages, they are absolute requirements for clinical trials. Concerning suggestions to improve the current hit success rate, one academic input in organic chemistry would be to identify new and pertinent chemical space, design synthetic accesses to reach these and prepare the corresponding chemical libraries. Concerning hit to lead programs on a given target, if no new hits are available, previously reported leads along with new structural data can be pertinent starting points to design, prepare and assay original analogues. In conclusion, this text is an actual plea illustrating that, in many countries, academic research in medicinal chemistry should be more funded, especially in the therapeutic area neglected by the industry. At the least, such funds would provide the intensive to secure series of hopefully relevant chemical entities which appears to often lack when considering the results of academic as well as industrial screening campaigns.

Selection of data sets for FAIRification in drug discovery and development: Which, why, and how?

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Research organisations are focussed on quantifying the costs and benefits of implementing FAIR.

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Criteria used for the selection of data for FAIRification can be opaque and inconsistent.

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FAIRification effort depends on individual skills, competencies, resources, and time available.

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FAIRification should satisfy reuse scenarios, and lead to scientific and economic impacts.

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Organisational challenges include providing training to individuals and developing a FAIR organisation culture.

Despite the intuitive value of adopting the Findable, Accessible, Interoperable, and Reusable (FAIR) principles in both academic and industrial sectors, challenges exist in resourcing, balancing long- versus short-term priorities, and achieving technical implementation. This situation is exacerbated by the unclear mechanisms by which costs and benefits can be assessed when decisions on FAIR are made. Scientific and research and development (R&D) leadership need reliable evidence of the potential benefits and information on effective implementation mechanisms and remediating strategies. In this article, we describe procedures for cost–benefit evaluation, and identify best-practice approaches to support the decision-making process involved in FAIR implementation.

Exploring the Current Practices, Costs and Benefits of FAIR Implementation in Pharmaceutical Research and Development: A Qualitative Interview Study

The findable, accessible, interoperable, reusable (FAIR) principles for scientific data management and stewardship aim to facilitate data reuse at scale by both humans and machines. Research and development (R&D) in the pharmaceutical industry is becoming increasingly data driven, but managing its data assets according to FAIR principles remains costly and challenging. To date, little scientific evidence exists about how FAIR is currently implemented in practice, what its associated costs and benefits are, and how decisions are made about the retrospective FAIRification of data sets in pharmaceutical R&D. This paper reports the results of semi-structured interviews with 14 pharmaceutical professionals who participate in various stages of drug R&D in seven pharmaceutical businesses. Inductive thematic analysis identified three primary themes of the benefits and costs of FAIRification, and the elements that influence the decision-making process for FAIRifying legacy data sets. Participants collectively acknowledged the potential contribution of FAIRification to data reusability in diverse research domains and the subsequent potential for cost-savings. Implementation costs, however, were still considered a barrier by participants, with the need for considerable expenditure in terms of resources, and cultural change. How decisions were made about FAIRification was influenced by legal and ethical considerations, management commitment, and data prioritisation. The findings have significant implications for those in the pharmaceutical R&D industry who are engaged in driving FAIR implementation, and for external parties who seek to better understand existing practices and challenges.

Powered by Open Innovation: Opportunities and Challenges in the Pharma Sector

The value of innovation in medicines is clear. Despite all of the progress in the twenty-first century, there are still many unmet medical needs and opportunities to improve healthcare. The challenges for pharmaceutical companies include ways in which to stay competitive and flexible in an environment of constant knowledge growth and increasingly sophisticated technologies, and ways to generate sufficient revenues to sustain their own growth. To that end, pharmaceutical companies are compelled to adapt different business models in the face of new challenges. The industry is plagued with long research and development (R&D) cycles and low success rates for innovative treatments; something has to change. The need to collaborate externally across the process of discovery, development, manufacturing and commercialization is a must. Furthermore, collaborations have increased in frequency and scope, expanding the opportunities to access global scientific talent in academia, research institutes and biotechnology companies. Despite the perception that pharma companies are 'closed' or tightly controlled industries, open innovation is already well established in the pharmaceutical sector and used to supplement R&D in the process of bringing new medicines for patients faster, and at a lower cost. Over the years, each pharma company has tailored the open-innovation concept to develop its own model based on particular needs and offerings. Independently of the model, the creation of successful partnerships in external innovation requires reaching out and connecting beyond the traditional organizational boundaries. Substantial internal cultural changes are required to implement open-innovation strategies that should co-exist without competing with the traditional ways of operating. Major changes bring challenges but create multiple opportunities for scientists and organizations. High-quality drug discovery requires continuous learning and an open way of thinking to adopt novel operational models and to implement efficient collaborations.

Implementation and relevance of FAIR data principles in biopharmaceutical R&D

Biopharmaceutical industry R&D, and indeed other life sciences R&D such as biomedical, environmental, agricultural and food production, is becoming increasingly data-driven and can significantly improve its efficiency and effectiveness by implementing the FAIR (findable, accessible, interoperable, reusable) guiding principles for scientific data management and stewardship. By so doing, the plethora of new and powerful analytical tools such as artificial intelligence and machine learning will be able, automatically and at scale, to access the data from which they learn, and on which they thrive. FAIR is a fundamental enabler for digital transformation.

Risk biomarkers enable precision in public health

Precision medicine uses biomarkers to diagnose disease. However, they can also be used to measure risk of disease. Thus, biomarkers herald a new addition to public health - Precision Public Health. We examine the implications. Risk biomarkers are identified by analyzing population cohorts. They constitute risk factors in mathematical 'Disease Risk Models'. The risk may be fixed as in a genetic biomarker or variable as in some protein biomarkers. They help monitor current risk of disease in an individual, thereby aiding efforts to reduce risk. In the UK, the NHS Health Check system is a universal system for assessing risk and for risk reduction. The system can now make use of modern biomarkers once appropriate infrastructure and governance are in place.

Open innovation and external sources of innovation. An opportunity to fuel the R&D pipeline and enhance decision making?

Historically, research and development (R&D) in the pharmaceutical sector has predominantly been an in-house activity. To enable investments for game changing late-stage assets and to enable better and less costly go/no-go decisions, most companies have employed a fail early paradigm through the implementation of clinical proof-of-concept organizations. To fuel their pipelines, some pioneers started to complement their internal R&D efforts through collaborations as early as the 1990s. In recent years, multiple extrinsic and intrinsic factors induced an opening for external sources of innovation and resulted in new models for open innovation, such as open sourcing, crowdsourcing, public–private partnerships, innovations centres, and the virtualization of R&D. Three factors seem to determine the breadth and depth regarding how companies approach external innovation: (1) the company’s legacy, (2) the company’s willingness and ability to take risks and (3) the company’s need to control IP and competitors. In addition, these factors often constitute the major hurdles to effectively leveraging external opportunities and assets. Conscious and differential choices of the R&D and business models for different companies and different divisions in the same company seem to best allow a company to fully exploit the potential of both internal and external innovations.

Working towards New Drugs against Parasitic Worms in a Public-Development Partnership

There is a clear need to develop new and inexpensive drugs to alleviate diseases caused by parasitic worms in animals and humans worldwide. In this article we discuss the roles and advantages of working in public-private partnerships (PPPs) - among academia, industry, and philanthropy - to enable anthelmintic drug discovery.

The Proteomics of Intrathecal Analgesic agents for Chronic Pain

Chronic pain remains a challenging clinical problem with a growing socio-economic burden for the state. Its prevalence is high and many of the patients are of work age. Our knowledge regarding the pathophysiology of chronic pain is poor. The consensus view is that the central nervous system plays a key role in the persistence of pain after an initiating event has long ceased. However the specifics of this biological response to an initiating event remains unclear. There is a growing body of evidence to support the concept that a central neuroimmune response is initiated and a number of small peptides have been implicated in this process following cerebrospinal fluid analysis in patients with chronic pain. This central biosynthetic peptide response leads to a process called central sensitization. Therapy is aimed at modulating and even inhibiting this response. However current pharmacological therapeutic options are limited in efficacy with significant deleterious side effect profiles. Proteomic studies extend single molecule analysis by identifying the components of biological networks and pathways and defining their interactions. This tool offers the potential to provide a molecular overview of the biological processes involved in chronic pain. It will also facilitate examination of gene-drug interactions. This technique offers a mechanism of defining the central biological responses that result in chronic pain and this information may facilitate the development of better therapies.

Post-Marketing Regulation of Medicines Withdrawn from the Market Because of Drug-Attributed Deaths: An Analysis of Justification

INTRODUCTION

Several medicinal products have been withdrawn from the market because of drug-attributed deaths. However, there has been no investigation of whether such withdrawals were justified, and the extent to which confirmatory studies are used to investigate drug-adverse event relationships when deaths are reported is uncertain. We documented medicinal products withdrawn from the market because of drug-attributed deaths, identified confirmatory studies investigating the drug-adverse event relationships, examined whether withdrawals of medicinal products because of drug-attributed deaths after marketing were justified based on a mechanistic analysis, and examined the trends over time.

METHODS

We searched electronic and non-electronic sources to identify medicinal products that were withdrawn because of drug-attributed deaths. We used a previously published algorithm to examine whether the withdrawals of products were justified. We then searched PubMed and Google Scholar to identify studies investigating the drug-adverse event relationships, used the Oxford Centre for Evidence-Based Medicine criteria to document the levels of evidence, and assessed whether the evidence of an association was confirmed.

RESULTS

We included 83 medicinal products. The reasons for withdrawal appeared to have been justified in 80 cases (96%). The median interval between the first reported adverse reaction that was related to the cause of death and the first reported death was 1 year (interquartile range = 1-3); products were withdrawn sooner when the interval between the first reported relevant adverse reaction and the first death was shorter. Confirmatory studies were conducted in 57 instances (69%), and there was evidence of an association in 52 cases (63%). Four products (5%) were re-introduced after initial withdrawal.

CONCLUSION

Regulatory authorities have been justified in making withdrawal decisions when deaths have been attributed to medicinal products, using the precautionary principle when alternative decisions could have been made. Medicinal products are likely to be quickly withdrawn from the market when there is a short interval to the first reported deaths. The use of an algorithm such as we have used in this study could help to expedite the process of decision making.

StemBANCC: Governing Access to Material and Data in a Large Stem Cell Research Consortium

This paper makes the case for implementing an internal governance framework for sharing materials and data in stem cell research consortia. A governance framework can facilitate a transparent and accountable system while building trust among partner institutions. However, avoiding excessive bureaucracy is essential. The development and implementation of a governance framework for materials and data access in the Stem cells for Biological Assays of Novel drugs and prediCtive toxiCology (StemBANCC) consortium is presented as a practical example. The StemBANCC project is a multi-partner European research consortium, which aims to build a resource of 1,500 well characterised induced pluripotent stem cell (iPSC) lines for in vitro disease modelling and toxicology studies. The project governance framework was developed in two stages. A small working group identified key components of a framework and translated the project legal agreements into a draft policy document. The second phase allowed input from all consortium partners to shape the iterative development of a final policy document that could be agreed by all parties. Careful time management strategies were needed to manage the duration of this component. This part of the process also served as an exploratory space where different options could be proposed, potential gaps in planning identified, and project co-ordination activities specified.

Open innovation in early drug discovery: roadmaps and roadblocks

Open innovation in pharmaceutical R&D evolved from a triple helix of convergent paradigm shifts in academic, industrial and government research sectors. The birth of the biotechnology sector catalyzed shifts in location dynamics that led to the first wave of open innovation in pharmaceutical R&D between big pharma and startup companies. The National Institutes of Health (NIH) Roadmap was a crucial inflection point that set the stage for a new wave of open innovation models between pharmaceutical companies and universities that have the potential to transform the pharmaceutical R&D landscape. We highlight the attributes of leading protected open innovation models that foster the sharing of proprietary small molecule collections by lowering the risk of premature escape of intellectual property, particularly structure-activity data.

Consortium sandbox: building and sharing resources

Some common challenges of biomedical product translation-scientific, regulatory, adoption, and reimbursement-can best be addressed by the broad sharing of resources or tools. But, such aids remain undeveloped because the undertaking requires expertise from multiple research sectors as well as validation across organizations. Biomedical resource development can benefit from directed consortia-a partnership framework that provides neutral and temporary collaborative environments for several, oftentimes competing, organizations and leverages the aggregated intellect and resources of stakeholders so as to create versatile solutions. By analyzing 369 biomedical research consortia, we tracked consortia growth around the world and gained insight into how this partnership model advances biomedical research. Our analyses suggest that research-by-consortium provides benefit to biomedical science, but the model needs further optimization before it can be fully integrated into the biomedical research pipeline.