A decade of innovation in pharmaceutical R&D: the Chorus model

Technology Readiness Level Roadmap for Developing Innovative Herbal Medicinal Products

Despite the vast global botanical diversity, the pharmaceutical development of herbal medicinal products (HMPs) remains underexploited. Of over 370,000 described plant species, only a few hundred are utilized in HMPs. Most of these have originated from traditional use, and only a minority come from megadiverse countries. Exploiting the pharmacological synergies of the hundreds of compounds found in poorly studied plant species may unlock new therapeutic possibilities, enhance megadiverse countries' scientific and socio-economic development, and help conserve biodiversity. However, extensive constraints in the development process of HMPs pose significant barriers to transforming this unsatisfactory socio-economic landscape. This paper proposes a roadmap to overcome these challenges, based on the technology readiness levels (TRLs) introduced by NASA to assess the maturity of technologies. It aims to assist research entities, manufacturers, and funding agencies from megadiverse countries in the discovery, development, and global market authorization of innovative HMPs that comply with regulatory standards from ANVISA, EMA, and FDA, as well as WHO and ICH guidelines.

Trends, challenges, and success factors in pharmaceutical portfolio management: Cognitive biases in decision-making and their mitigating measures

Effective portfolio management is crucial for innovation and sustaining revenue in pharmaceutical companies. This article holistically reviews trends, challenges, and approaches to pharmaceutical portfolio management and focuses, in particular, on cognitive biases in portfolio decision-making. Portfolio managers strongly rely on external innovation and face increasing competitive pressure and portfolio complexity. The ability to address biases and make robust decisions remains a challenge. Portfolio management practitioners most commonly face confirmation bias, champion bias, or misaligned incentives, which they seek to mitigate through expert input, team diversity, and rewarding truth-seeking. Ultimately, highest-quality portfolio management decision-making could be enabled by three factors: high-quality data, structured review processes, and comprehensive mitigating measures against biases in decision-making.

In vivo human keyhole limpet hemocyanin challenge in early phase drug development: A systematic review

Experimental exposure of healthy volunteers to the T-cell dependent neoantigen keyhole limpet hemocyanin (KLH) permits the evaluation of immunomodulatory investigational medicinal product (IMP) pharmacology prior to the recruitment of patient populations. Despite widespread use, no standardized approach to the design and conduct of such studies has been agreed. The objective of this systematic review was to survey the published literature where KLH was used as a challenge agent, describing methodology, therapeutic targets addressed, and pharmacodynamic outcome measures. We searched MEDLINE, EMBASE, clinicaltrials.gov, and Cochrane CENTRAL for studies using KLH challenge in humans between January 1, 1994, and April 1, 2022. We described key study features, including KLH formulation, dose, use of adjuvants, route of administration, co-administered IMPs, and end points. Of 2421 titles and abstracts screened, 46 met the inclusion criteria, including 14 (31%) early phase trials of IMP, of which 10 (71%) targeted T-cell co-stimulation. IMPs with diverse mechanisms demonstrated modulation of the humoral response to KLH, suggesting limited specificity of this end point. Two early phase IMP studies (14%) described the response to intradermal re-challenge (delayed type hypersensitivity). Challenge regimens for IMP assessment were often incompletely described, and exhibited marked heterogeneity, including primary KLH dose (25-fold variation: 100-2500 mcg), KLH formulation, and co-administration with adjuvants. Methodological heterogeneity and failure to exploit the access to tissue-level mechanism-relevant end points afforded by KLH challenge has impaired the translational utility of this paradigm to date. Future standardization, characterization, and methodological development is required to permit tailored, appropriately powered, mechanism-dependent study design to optimize drug development decisions.

Supporting Innovation in Early-Stage Pharmaceutical Development Decisions

Pharmaceutical companies have frequent portfolio reviews to monitor development progress and prioritize development assets. The earliest assets are drug candidates whose efficacy is unknown and whose effects on the human body have yet to be fully investigated. These assets are characterized by a high degree of uncertainty in reaching the market and in being used in clinical practice. In addition, not all potential applications are foreseen and can often be very different. In the absence of satisfactory methods for making decisions on resource allocation among early development assets, decision makers focus almost exclusively on assessments of an asset’s probability of technical success. This study proposes a more holistic methodology to support early-stage pharmaceutical development decisions using value-focused thinking and multicriteria decision making. The methodology operates within the decision quality framework and provides a consistent evaluation of various early development assets across a diverse set of disease areas. This combination of concepts and methodologies has been implemented and proven valuable at Bayer Pharmaceuticals, which needed a new, more robust decision-making process for early development. Thus, this study discusses how to enable concrete trade-offs at the level of corporate objectives to align, communicate, and translate corporate strategy into portfolio strategy. In addition, this study presents learnings for decision analysts and decision makers in the pharmaceutical industry on how to develop a set of fundamental objectives, how to create scales to operationalize these objectives, and how to take steps to debias an organizational decision-making process.

Achieving end-to-end success in the clinic: Pfizer's learnings on R&D productivity

Over the past decade, Pfizer has focused efforts to improve its research and development (R&D) productivity. By the end of 2020, Pfizer had achieved an industry-leading clinical success rate of 21%, a tenfold increase from 2% in 2010 and well above the industry benchmark of ∼11%. The company had also maintained the quality of innovation, because 75% of its approvals between 2016 and 2020 had at least one expedited regulatory designation (e.g., Breakthrough Therapy). Pfizer's Signs of Clinical Activity (SOCA) paradigm enabled better decision-making and, along with other drivers (biology and modality), contributed to this productivity improvement. These laid a strong foundation for the rapid and effective development of the Coronavirus 2019 (COVID-19) vaccine with BioNTech, as well as the antiviral candidate Paxlovid™, under the company's 'lightspeed' paradigm.

R&D efficiency of leading pharmaceutical companies - A 20-year analysis

Comparative analysis of the R&D efficiency of 14 leading pharmaceutical companies for the years 1999-2018 shows that there is a close positive correlation between R&D spending and the two investigated R&D output parameters, approved NMEs and the cumulative impact factor of their publications. In other words, higher R&D investments (input) were associated with higher R&D output. Second, our analyses indicate that there are 'economies of scale' (size) in pharmaceutical R&D.

Value-Generating Exploratory Trials in Neurodegenerative Dementias

Drug development for Alzheimer disease and other neurodegenerative dementias, including frontotemporal dementia, has experienced a long history of phase 2 and phase 3 clinical trials that failed to show efficacy of investigational drugs. Despite differences in clinical and behavioral characteristics, these disorders have shared pathologies and face common challenges in designing early-phase trials that are predictive of late-stage success. Here, we discuss exploratory clinical trials in neurodegenerative dementias. These are generally phase 1b or phase 2a trials that are designed to assess pharmacologic effects and rely on biomarker outcomes, with shorter treatment durations and fewer patients than traditional phase 2 studies. Exploratory trials can establish go/no-go decision points, support proof of concept and dose selection, and terminate drugs that fail to show target engagement with suitable exposure and acceptable safety profiles. Early failure saves valuable resources including opportunity costs. This is especially important for programs in academia and small biotechnology companies but may be applied to high-risk projects in large pharmaceutical companies to achieve proof of concept more rapidly at lower costs than traditional approaches. Exploratory studies in a staged clinical development program may provide promising data to warrant the substantial resources needed to advance compounds through late-stage development. To optimize the design and application of exploratory trials, the Alzheimer's Drug Discovery Foundation and the Association for Frontotemporal Degeneration convened an advisory panel to provide recommendations on outcome measures and statistical considerations for these types of studies and study designs that can improve efficiency in clinical development.

Phase 0/microdosing approaches: time for mainstream application in drug development?

Phase 0 approaches - which include microdosing - evaluate subtherapeutic exposures of new drugs in first-in-human studies known as exploratory clinical trials. Recent progress extends phase 0 benefits beyond assessment of pharmacokinetics to include understanding of mechanism of action and pharmacodynamics. Phase 0 approaches have the potential to improve preclinical candidate selection and enable safer, cheaper, quicker and more informed developmental decisions. Here, we discuss phase 0 methods and applications, highlight their advantages over traditional strategies and address concerns related to extrapolation and developmental timelines. Although challenges remain, we propose that phase 0 approaches be at least considered for application in most drug development scenarios.

A randomized clinical efficacy study targeting mPGES1 or EP4 in dogs with spontaneous osteoarthritis

Canine studies of spontaneous osteoarthritis (OA) pain add valuable data supporting drug treatment mechanisms that may translate to humans. A multicenter, randomized, double-blind, placebo- and active-controlled study was conducted in client-owned dogs with moderate OA pain to evaluate efficacy of LYA, an inhibitor of microsomal prostaglandin E synthase-1 (mPGES1), an EP4 antagonist (LYB), and carprofen, versus placebo. Of 255 dogs screened, 163 were randomized (placebo/LYA/LYB/carprofen: n = 43/39/42/39) and 158 completed treatment. Efficacy versus placebo was assessed using Bayesian mixed-effect model for repeated measure analyses of the Canine Brief Pain Inventory (CBPI) pain interference score (PIS; primary endpoint), pain severity score, and overall impression, as well as the Liverpool Osteoarthritis in Dogs (LOAD) mobility score. The posterior probability that the difference to placebo was <0 at week 2 was 80% for LYA and 54% for LYB for CBPI PIS (both <95% predefined threshold). For secondary endpoints, the posterior probability that the difference to placebo was <0 at week 2 ranged from 89 to 96% for LYA and from 56 to 89% for LYB. The posterior probabilities comparing carprofen to placebo groups were ≥90% for all efficacy endpoints. The proportion of dogs with one or more adverse event was not significantly different from placebo (32.6%) for LYA (35.9%) or carprofen (25.6%), but the rate for LYB (59.5%) was higher versus placebo (P = 0.017). LYA treatment demonstrated consistent improvement in all efficacy measures, suggesting that inhibition of mPGES1 may be an effective treatment for chronic pain associated with OA.

Technological Advances in Preclinical Drug Evaluation: The Role of -Omics Methods

Preclinical drug development is an essential step in the drug development process where the evaluation of new chemical entities occurs. In particular, preclinical drug development phases include deep analysis of drug candidates' interactions with biomolecules/targets, their safety, toxicity, pharmacokinetics, metabolism by use of assays in vitro and in vivo animal assays. Legal aspects of the required procedures are well-established. Herein, we present a comprehensive summary of current state-of-the art approaches and techniques used in preclinical studies. In particular, we will review the potential of new, -omics methods and platforms for mechanistic evaluation of drug candidates and speed-up of the preclinical evaluation steps.

Expedited Safety Reporting to Sponsors Through the Implementation of an Alert System for Clinical Trial Management at an Academic Medical Center: Retrospective Design Study

Background

Early detection or notification of adverse event (AE) occurrences during clinical trials is essential to ensure patient safety. Clinical trials take advantage of innovative strategies, clinical designs, and state-of-the-art technologies to evaluate efficacy and safety, however, early awareness of AE occurrences by investigators still needs to be systematically improved.

Objective

This study aimed to build a system to promptly inform investigators when clinical trial participants make unscheduled visits to the emergency room or other departments within the hospital.

Methods

We developed the Adverse Event Awareness System (AEAS), which promptly informs investigators and study coordinators of AE occurrences by automatically sending text messages when study participants make unscheduled visits to the emergency department or other clinics at our center. We established the AEAS in July 2015 in the clinical trial management system. We compared the AE reporting timeline data of 305 AE occurrences from 74 clinical trials between the preinitiative period (December 2014-June 2015) and the postinitiative period (July 2015-June 2016) in terms of three AE awareness performance indicators: onset to awareness, awareness to reporting, and onset to reporting.

Results

A total of 305 initial AE reports from 74 clinical trials were included. All three AE awareness performance indicators were significantly lower in the postinitiative period. Specifically, the onset-to-reporting times were significantly shorter in the postinitiative period (median 1 day [IQR 0-1], mean rank 140.04 [SD 75.35]) than in the preinitiative period (median 1 day [IQR 0-4], mean rank 173.82 [SD 91.07], P≤.001). In the phase subgroup analysis, the awareness-to-reporting and onset-to-reporting indicators of phase 1 studies were significantly lower in the postinitiative than in the preinitiative period (preinitiative: median 1 day, mean rank of awareness to reporting 47.94, vs postinitiative: median 0 days, mean rank of awareness to reporting 35.75, P=.01; and preinitiative: median 1 day, mean rank of onset to reporting 47.4, vs postinitiative: median 1 day, mean rank of onset to reporting 35.99, P=.03). The risk-level subgroup analysis found that the onset-to-reporting time for low- and high-risk studies significantly decreased postinitiative (preinitiative: median 4 days, mean rank of low-risk studies 18.73, vs postinitiative: median 1 day, mean rank of low-risk studies 11.76, P=.02; and preinitiative: median 1 day, mean rank of high-risk studies 117.36, vs postinitiative: median 1 day, mean rank of high-risk studies 97.27, P=.01). In particular, onset to reporting was reduced more in the low-risk trial than in the high-risk trial (low-risk: median 4-0 days, vs high-risk: median 1-1 day).

Conclusions

We demonstrated that a real-time automatic alert system can effectively improve safety reporting timelines. The improvements were prominent in phase 1 and in low- and high-risk clinical trials. These findings suggest that an information technology-driven automatic alert system effectively improves safety reporting timelines, which may enhance patient safety.

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.

Exploring the new horizons of drug repurposing: A vital tool for turning hard work into smart work

Drug discovery and development are long and financially taxing processes. On an average it takes 12-15 years and costs 1.2 billion USD for successful drug discovery and approval for clinical use. Many lead molecules are not developed further and their potential is not tapped to the fullest due to lack of resources or time constraints. In order for a drug to be approved by FDA for clinical use, it must have excellent therapeutic potential in the desired area of target with minimal toxicities as supported by both pre-clinical and clinical studies. The targeted clinical evaluations fail to explore other potential therapeutic applications of the candidate drug. Drug repurposing or repositioning is a fast and relatively cheap alternative to the lengthy and expensive de novo drug discovery and development. Drug repositioning utilizes the already available clinical trials data for toxicity and adverse effects, at the same time explores the drug's therapeutic potential for a different disease. This review addresses recent developments and future scope of drug repositioning strategy.

Development of Human Target Validation Classification that Predicts Future Clinical Efficacy

Fewer new medicines have become available to patients during the last decades. Clinical efficacy failures in late-phase development have been identified as a common cause of this decline. Improved ways to ensure early selection of the right drug targets when it comes to efficacy is therefore a highly desirable goal. The aim of this work was to develop a strategy to facilitate selection of novel targets already in the discovery phase that later on in clinical development would demonstrate efficacy. A cross-functional team at AstraZeneca with extensive experience in drug discovery and development participated in several workshops to identify the critical elements that contribute to building human target validation [(HTV); the relevance of the target from a human perspective]. The elements were consolidated into a 10-point HTV classification system that was ranked from lowest to highest in terms of perceived impact on future clinical efficacy. Using 50 years of legacy research and development data, the ability of the 10-point HTV classification to predict future clinical efficacy was evaluated. Drug targets were classified as having low, medium, or high HTV at the time of candidate drug selection. Comparing this HTV classification with later clinical development efficacy data showed that HTV classification was highly predictive of future clinical efficacy success. This new strategy for HTV assessment provides a novel approach to early prediction of clinical efficacy and a better understanding of portfolio risk.

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.

Integrating dose estimation into a decision-making framework for model-based drug development

Model-informed drug discovery and development offers the promise of more efficient clinical development, with increased productivity and reduced cost through scientific decision making and risk management. Go/no-go development decisions in the pharmaceutical industry are often driven by effect size estimates, with the goal of meeting commercially generated target profiles. Sufficient efficacy is critical for eventual success, but the decision to advance development phase is also dependent on adequate knowledge of appropriate dose and dose-response. Doses which are too high or low pose risk of clinical or commercial failure. This paper addresses this issue and continues the evolution of formal decision frameworks in drug development. Here, we consider the integration of both efficacy and dose-response estimation accuracy into the go/no-go decision process, using a model-based approach. Using prespecified target and lower reference values associated with both efficacy and dose accuracy, we build a decision framework to more completely characterize development risk. Given the limited knowledge of dose response in early development, our approach incorporates a set of dose-response models and uses model averaging. The approach and its operating characteristics are illustrated through simulation. Finally, we demonstrate the decision approach on a post hoc analysis of the phase 2 data for naloxegol (a drug approved for opioid-induced constipation).

Disciplined approach to drug discovery and early development

Our modern health care system demands therapeutic interventions that improve the lives of patients. Unfortunately, decreased productivity in therapeutics research and development (R&D) has driven drug costs up while delivering insufficient value to patients. Here, I discuss a model of translational medicine that connects four components of the early R&D pipeline-causal human biology, therapeutic modality, biomarkers of target modulation, and proof-of-concept clinical trials. Whereas the individual components of this model are not new, technological advances and a disciplined approach to integrating all four areas offer hope for improving R&D productivity.

Has molecular imaging delivered to drug development?

Pharmaceutical research and development requires a systematic interrogation of a candidate molecule through clinical studies. To ensure resources are spent on only the most promising molecules, early clinical studies must understand fundamental attributes of the drug candidate, including exposure at the target site, target binding and pharmacological response in disease. Molecular imaging has the potential to quantitatively characterize these properties in small, efficient clinical studies. Specific benefits of molecular imaging in this setting (compared to blood and tissue sampling) include non-invasiveness and the ability to survey the whole body temporally. These methods have been adopted primarily for neuroscience drug development, catalysed by the inability to access the brain compartment by other means. If we believe molecular imaging is a technology platform able to underpin clinical drug development, why is it not adopted further to enable earlier decisions? This article considers current drug development needs, progress towards integration of molecular imaging into studies, current impediments and proposed models to broaden use and increase impact.This article is part of the themed issue 'Challenges for chemistry in molecular imaging'.

Drug discovery and development: Role of basic biological research

This article provides a brief overview of the processes of drug discovery and development. Our aim is to help scientists whose research may be relevant to drug discovery and/or development to frame their research report in a way that appropriately places their findings within the drug discovery and development process and thereby support effective translation of preclinical research to humans. One overall theme of our article is that the process is sufficiently long, complex, and expensive so that many biological targets must be considered for every new medicine eventually approved for clinical use and new research tools may be needed to investigate each new target. Studies that contribute to solving any of the many scientific and operational issues involved in the development process can improve the efficiency of the process. An awareness of these issues allows the early implementation of measures to increase the opportunity for success. As editors of the journal, we encourage submission of research reports that provide data relevant to the issues presented.

An iterative compound screening contest method for identifying target protein inhibitors using the tyrosine-protein kinase Yes

We propose a new iterative screening contest method to identify target protein inhibitors. After conducting a compound screening contest in 2014, we report results acquired from a contest held in 2015 in this study. Our aims were to identify target enzyme inhibitors and to benchmark a variety of computer-aided drug discovery methods under identical experimental conditions. In both contests, we employed the tyrosine-protein kinase Yes as an example target protein. Participating groups virtually screened possible inhibitors from a library containing 2.4 million compounds. Compounds were ranked based on functional scores obtained using their respective methods, and the top 181 compounds from each group were selected. Our results from the 2015 contest show an improved hit rate when compared to results from the 2014 contest. In addition, we have successfully identified a statistically-warranted method for identifying target inhibitors. Quantitative analysis of the most successful method gave additional insights into important characteristics of the method used.

The Burden of the "False-Negatives" in Clinical Development: Analyses of Current and Alternative Scenarios and Corrective Measures

The “false‐negatives” of clinical development are the effective treatments wrongly determined ineffective. Statistical errors leading to “false‐negatives” are larger than those leading to “false‐positives,” especially in typically underpowered early‐phase trials. In addition, “false‐negatives” are usually eliminated from further testing, thereby limiting the information available on them. We simulated the impact of early‐phase power on economic productivity in three developmental scenarios. Scenario 1, representing the current status quo, assumed 50% statistical power at phase II and 90% at phase III. Scenario 2 assumed increased power (80%), and Scenario 3, increased stringency of alpha (1%) at phase II. Scenario 2 led, on average, to a 60.4% increase in productivity and 52.4% increase in profit. Scenario 3 had no meaningful advantages. Our results suggest that additional costs incurred by increasing the power of phase II studies are offset by the increase in productivity. We discuss the implications of our results and propose corrective measures.

Shaking Up Biotech/Pharma: Can Cues Be Taken from the Tech Industry?

The biotech and pharma (biotech/pharma) industry is in dire need of finding ways to increase efficacy, efficiency, and affordability of its products. The information and technology industry ("tech") industry, which is an industry similarly founded on inventions and innovation, may provide some food for thought. This perspective will demonstrate the shift that biotech/pharma is already making and will propose that the industry has an opportunity to compel change by adopting some aspects of the tech industry with regards to models for technology/product development and leadership attributes.

Measuring the effectiveness and impact of an open innovation platform

Today, most pharmaceutical companies complement their traditional R&D models with some variation on the Open Innovation (OI) approach in an effort to better access global scientific talent, ideas and hypotheses. Traditional performance indicators that measure economic returns from R&D through commercialization are often not applicable to the practical assessment of these OI approaches, particularly within the context of early drug discovery. This leaves OI programs focused on early R&D without a standard assessment framework from which to evaluate overall performance. This paper proposes a practical dashboard for such assessment, encompassing quantitative and qualitative elements, to enable decision-making and improvement of future performance. The use of this dashboard is illustrated using real-time data from the Lilly Open Innovation Drug Discovery (OIDD) program.

Opportunities and Challenges for Drug Development: Public-Private Partnerships, Adaptive Designs and Big Data

Drug development faces the double challenge of increasing costs and increasing pressure on pricing. To avoid that lack of perceived commercial perspective will leave existing medical needs unmet, pharmaceutical companies and many other stakeholders are discussing ways to improve the efficiency of drug Research and Development. Based on an international symposium organized by the Medical School of the University of Duisburg-Essen (Germany) and held in January 2016, we discuss the opportunities and challenges of three specific areas, i.e., public–private partnerships, adaptive designs and big data. Public–private partnerships come in many different forms with regard to scope, duration and type and number of participants. They range from project-specific collaborations to strategic alliances to large multi-party consortia. Each of them offers specific opportunities and faces distinct challenges. Among types of collaboration, investigator-initiated studies are becoming increasingly popular but have legal, ethical, and financial implications. Adaptive trial designs are also increasingly discussed. However, adaptive should not be used as euphemism for the repurposing of a failed trial; rather it requires carefully planning and specification before a trial starts. Adaptive licensing can be a counter-part of adaptive trial design. The use of Big Data is another opportunity to leverage existing information into knowledge useable for drug discovery and development. Respecting limitations of informed consent and privacy is a key challenge in the use of Big Data. Speakers and participants at the symposium were convinced that appropriate use of the above new options may indeed help to increase the efficiency of future drug development.

Complementary Approaches to Existing Target Based Drug Discovery for Identifying Novel Drug Targets

In the past decade, it was observed that the relationship between the emerging New Molecular Entities and the quantum of R&D investment has not been favorable. There might be numerous reasons but few studies stress the introduction of target based drug discovery approach as one of the factors. Although a number of drugs have been developed with an emphasis on a single protein target, yet identification of valid target is complex. The approach focuses on an in vitro single target, which overlooks the complexity of cell and makes process of validation drug targets uncertain. Thus, it is imperative to search for alternatives rather than looking at success stories of target-based drug discovery. It would be beneficial if the drugs were developed to target multiple components. New approaches like reverse engineering and translational research need to take into account both system and target-based approach. This review evaluates the strengths and limitations of known drug discovery approaches and proposes alternative approaches for increasing efficiency against treatment.

Changing R&D models in research-based pharmaceutical companies

New drugs serving unmet medical needs are one of the key value drivers of research-based pharmaceutical companies. The efficiency of research and development (R&D), defined as the successful approval and launch of new medicines (output) in the rate of the monetary investments required for R&D (input), has declined since decades. We aimed to identify, analyze and describe the factors that impact the R&D efficiency. Based on publicly available information, we reviewed the R&D models of major research-based pharmaceutical companies and analyzed the key challenges and success factors of a sustainable R&D output. We calculated that the R&D efficiencies of major research-based pharmaceutical companies were in the range of USD 3.2–32.3 billion (2006–2014). As these numbers challenge the model of an innovation-driven pharmaceutical industry, we analyzed the concepts that companies are following to increase their R&D efficiencies: (A) Activities to reduce portfolio and project risk, (B) activities to reduce R&D costs, and (C) activities to increase the innovation potential. While category A comprises measures such as portfolio management and licensing, measures grouped in category B are outsourcing and risk-sharing in late-stage development. Companies made diverse steps to increase their innovation potential and open innovation, exemplified by open source, innovation centers, or crowdsourcing, plays a key role in doing so. In conclusion, research-based pharmaceutical companies need to be aware of the key factors, which impact the rate of innovation, R&D cost and probability of success. Depending on their company strategy and their R&D set-up they can opt for one of the following open innovators: knowledge creator, knowledge integrator or knowledge leverager.

Sources of Information as Determinants of Product and Process Innovation

In this paper we use a panel of manufacturing firms in Spain to examine the extent to which they use internal and external sources of information (customers, suppliers, competitors, consultants and universities) to generate product and process innovation. Our results show that, although internal sources are influential, external sources of information are key to achieve innovation performance. These results are in line with the open innovation literature because they show that firms that are opening up their innovation process and that use different information sources have a greater capacity to generate innovations. We also find that the importance of external sources of information varies depending on the type of innovation (product or process) considered. To generate process innovation, firms mainly rely on suppliers while, to generate product innovation, the main contribution is from customers. The potential simultaneity between product and process innovation is also taken into consideration. We find that the generation of both types of innovation is not independent.

Bridging academic science and clinical research in the search for novel targeted anti-cancer agents

This review starts with a brief history of drug discovery & development, and the place of Asia in this worldwide effort discussed. The conditions and constraints of a successful translational R&D involving academic basic research and clinical research are discussed and the Singapore model for pursuit of open R&D described. The importance of well-characterized, validated drug targets for the search for novel targeted anti-cancer agents is emphasized, as well as a structured, high quality translational R&D. Furthermore, the characteristics of an attractive preclinical development drug candidate are discussed laying the foundation of a successful preclinical development. The most frequent sources of failures are described and risk management at every stage is highly recommended. Organizational factors are also considered to play an important role. The factors to consider before starting a new drug discovery & development project are described, and an example is given of a successful clinical project that has had its roots in local universities and was carried through preclinical development into phase I clinical trials.

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.

Why is it hard to terminate failing projects in pharmaceutical R&D?

'Quick-kill' strategies in pharmaceutical research and development aim to reduce late-stage attrition by bringing project termination decisions forward, to an earlier point in the process. How can the barriers to implementing such strategies be overcome?

Genetics of human metabolism: an update

Genome-wide association studies with metabolomics (mGWAS) identify genetically influenced metabotypes (GIMs), their ensemble defining the heritable part of every human's metabolic individuality. Knowledge of genetic variation in metabolism has many applications of biomedical and pharmaceutical interests, including the functional understanding of genetic associations with clinical end points, design of strategies to correct dysregulations in metabolic disorders and the identification of genetic effect modifiers of metabolic disease biomarkers. Furthermore, it has been shown that GIMs provide testable hypotheses for functional genomics and metabolomics and for the identification of novel gene functions and metabolite identities. mGWAS with growing sample sizes and increasingly complex metabolic trait panels are being conducted, allowing for more comprehensive and systems-based downstream analyses. The generated large datasets of genetic associations can now be mined by the biomedical research community and provide valuable resources for hypothesis-driven studies. In this review, we provide a brief summary of the key aspects of mGWAS, followed by an update of recently published mGWAS. We then discuss new approaches of integrating and exploring mGWAS results and finish by presenting selected applications of GIMs in recent studies.

Accessing external innovation in drug discovery and development

INTRODUCTION

A decline in the productivity of the pharmaceutical industry research and development (R&D) pipeline has highlighted the need to reconsider the classical strategies of drug discovery and development, which are based on internal resources, and to identify new means to improve the drug discovery process. Accepting that the combination of internal and external ideas can improve innovation, ways to access external innovation, that is, opening projects to external contributions, have recently been sought.

AREAS COVERED

In this review, the authors look at a number of external innovation opportunities. These include increased interactions with academia via academic centers of excellence/innovation centers, better communication on projects using crowdsourcing or social media and new models centered on external providers such as built-to-buy startups or virtual pharmaceutical companies.

EXPERT OPINION

The buzz for accessing external innovation relies on the pharmaceutical industry's major challenge to improve R&D productivity, a conjuncture favorable to increase interactions with academia and new business models supporting access to external innovation. So far, access to external innovation has mostly been considered during early stages of drug development, and there is room for enhancement. First outcomes suggest that external innovation should become part of drug development in the long term. However, the balance between internal and external developments in drug discovery can vary largely depending on the company strategies.