Innovation in Life Sciences

Innovation in the life sciences emerges from the tension of science facts, science fiction, societal expectations, and the viability of business models. We apply science fiction for inspiration and reflection of life science innovators in a dialectical way between biopunk and solarpunk. Starting with the dystopian biopunk genre, we exemplify various trends and patterns from a critical perspective on the life sciences, focusing on bioengineering and artificial intelligence. We derive inspirations and conclusions for a responsible innovation process. The outlook into the optimistic solarpunk genre offers more optimism and practical inspiration. We finally advocate science fiction labs in the innovation process of the life science industry to metaphorically bring biopunks and solarpunks to dance.

Significant progress has been made in several areas of our lives within the last few decades: machine learning and artificial intelligence methods, data storage and transmission approaches, computational hardware, biotech, and drug discovery. All these advances combined unlock the potential for artificial intelligence to become a paradigm-shifting technology for drug discovery. Current obstacles in traditional drug discovery, the potential impact of AI on drug discovery, core differentiating factors of AI over traditional algorithms, and the latest advances in drug discovery with AI are discussed in this chapter.

Life Cycle Management of pharmaceutical products consists of many measures aiming to speed up time to and on the market and clinical use while the drug enjoys patent protection.

Among many other factors, LCM decisions ought to consider technology changes that affect diagnostic or clinical pathways in indications covered by the approved pharmaceutical drug.

The current paper discusses an Investigator Sponsored Research (ISR) activity financially supported by the manufacturer of an oral anticoagulant drug. It was a randomized, double-blind siteless clinical trial that successfully established feasibility and effectiveness of a digitally enabled innovative diagnostic pathway. Tools and pathways tested in the trial greatly facilitated detection and subsequent treatment of patients with Atrial Fibrillation which is the main indication of the oral anticoagulant marketed by the pharmaceutical company.

Therefore, the tool and pathway researched in the trial have become a relevant factor in the life cycle management of this prescription drug.

Using this study as an example the paper comprehensively discusses circumstances and conditions needed to successfully leverage external factors like digital technologies for the life cycle management of a classic prescription drug.

Biopharmaceutical drug modalities derived from animal cell culture are inherently complex and heterogeneous by nature. Among the most complex of these biologic entities are therapeutic glycoproteins and glycosylated viral vectors employed in Advanced Therapeutic Medicinal Products (ATMPs). The specific profile of the glycans attached to these modalities constitutes a relevant critical quality attribute (CQA) of such biologics.

Data on the structure and composition of these product-associated glycans are the most difficult to obtain at high quality and throughput. In combination with the wealth of data obtained routinely for the tightly controlled process variables during upstream production, such detailed glycan data are much needed to enable artificial intelligence tools to derive information and knowledge for the optimization of product glycosylation during biomanufacturing.

In this contribution, recent developments in this field will be discussed.

The evolution of several digital technologies and their convergence as well as more global changes in the healthcare ecosystems, seem to fuel the digital transformation. The change in process development and manufacturing could be fundamental, and the digital transformation more of revolutionary nature with consequences even for the companies' positioning in the value chain. This contribution discusses recent trends, implications, and potential future digital process development and production scenarios are sketched. Approaches for the digital transformation journey are discussed and recent evolutions in the ecosystem in light of potential ambitions are analyzed.

High-throughput experimentation systems advanced (online) sensor technologies, high-resolution product analytics, and advanced and automated data analytics are the basis for next-generation bioprocess research and development in the Pharmaceutical industry. The need for an end-to-end data infrastructure is often neglected but fundamental in order to unleash the full potential of the generated data.

This case study showcases the digital evolution of our laboratories in Bioprocess Research at Roche Pharma Research and Early Development (pRED), starting with an ELN (Electronic Laboratory Notebook) based approach in the center of the IT landscape. In the last decade laboratory, automation and high-throughput experimentation progressed dramatically, to the point where data collection and efficient storage were becoming the bottleneck in the process. We introduced some major changes to the IT architecture to fit the new data types and to enable the collection of high amounts of well-contextualized data to meet data analytics needs. Our new IT landscape is a microservice-based architecture, which allows us to reuse and build upon key functional systems. At the core sits an in-house developed experiment management tool (Experiment Manager) as a new user-centric platform for bioprocess data management, with the capability of executing workflow orchestration routines for laboratory automation.

By eliminating major technical debts from legacy systems and setting up the basis for a new infrastructure we enabled the easy implementation of laboratory and data automation routines as well as providing analytical dashboards and programmatic data access for cross-functional and cross-process scale data analytics, modeling, and data science.

Hand surgery is a highly specialized field that focuses on treating hand and upper extremity conditions, which can be challenging due to the complex anatomy and biomechanics of the hand. Patient-specific treatment is essential in hand surgery, as each patient has unique anatomical, physiological, and psychological factors that must be considered when planning and executing surgical interventions. Advances in technology and surgical techniques have enabled hand surgeons to offer more personalized treatment options to their patients, such as 3D-printed implants and minimally invasive procedures. Additionally, hand therapists play an essential role in post-operative care, providing individualized rehabilitation programs to optimize the patient’s functional recovery.

By tailoring treatment plans to the patient’s unique needs, hand surgeons can achieve better outcomes and improve the patient’s quality of life.

More than 20 years have passed since WHO stated obesity as a disease and included obesity in the cluster of non-communicable diseases requiring prevention and management strategies at individual and societal levels. During that time, intensive research on the subject was performed. Still, the prevalence of obesity continues to rise in Western and developing countries, with an increasing burden on the healthcare systems. Much remains to be done to deliver relevant national plans for the prevention, management, and sustainable treatment of obesity. The recognition that obesity is a multifactorial, chronic, relapsing disease requiring long-term management with whole-system support makes it challenging to design successful and sustainable therapy modalities. This chapter explores the extent to which digital interventions can solve the problem of the obesity crisis. Technological advances like mobile health, wearable devices, and the Internet of Medical Things (IoMT) offer emerging opportunities to improve quality and increase the success of obesity therapy. Artificial intelligence (AI) and machine learning algorithms (ML) are required for increasingly automated, personalized interventions and long-term patient support. With the complexity of the human body and its physiology, innovative personalized medicine approaches are summarized, including the microbiome, genetic and epigenetic factors, metabolomics, and clinical markers. Finally, the adoption of digital interventions in obesity care is critically reflected. Digital innovations combined with personalized medicine can potentially stop our pandemic of chronic diseases and obesity if applied effectively, efficiently, and patient-oriented.

Deep learning and especially convolutional neural networks (CNN) have established themselves as state-of-the-art methods in the field of image and object detection throughout the last decade. In healthcare they are successfully used, for example, to detect skin or breast cancer, where they reach the level of an expert opinion. In musculoskeletal imaging, there is a wide range of tasks which can be taken over by machine learning methods. While there are many advanced algorithms in the field of two-dimensional imaging (X-rays) showing strong performances, machine learning in three-dimensional imaging (e.g., computed tomography) shows promising results yet is at an earlier stage. This chapter gives an overview of current applications in two- and three-dimensional medical imaging and also highlights some ethical, moral, legal, and socio-economic aspects of this rapidly progressing field.

The Fourth Industrial Revolution is in full bloom. Digital technologies like artificial intelligence (AI), machine learning (ML), and robotics, to name only a few, have emerged from future fantasies. With its growing importance in our daily lives, the healthcare sector has become an important research arena for scholars and practitioners. Our research study investigates in the recent technology developments, represented by startups from the life sciences. We approached the research by understanding the interplay of digital health stakeholders forming the startup ecosystem. We have identified five clusters by studying the last decades and recent founding activities in the USA and Germany. First-mover organizations in biotechnology, medical devices, diagnostics, and IT services have guided the research. In our research study, 30 organizations have been portrayed to draw a recent picture of the digital startup landscape.

Innovation is a key driver of success in the life sciences industry, as it allows companies to develop new products, processes, and services that can improve patient outcomes and generate faster and better management of healthcare challenges. As evidenced by the recent COVID-19 outbreak and healthcare responses, emerging big data, digitization of processes and services, and increased cloud collaboration have reduced innovation costs while providing healthcare solutions at breathtaking speeds. This enables key stakeholders (academia, biotechnology/med technology startups, pharmaceutical companies, manufacturers and investors, etc.) to invest intelligently in future life sciences industry disruptors, and builds resilience to prevent future disruptions due to health emergencies. However, to manage such a rapidly changing innovation scenario and adaptation in life sciences, it is necessary to have a strategic approach that aligns with a company’s overall business goals, and at the same time, provides effective and efficient solutions to contribute to resilient innovation for the future. In this chapter, we will be discussing the key challenges in handling innovation pipelines involving big data and artificial intelligence, some case studies we have experienced, and key business strategies we have adopted to navigate our way in the field of innovation in life sciences.

This contribution presents a lean approach to software design, focusing on the journey from a basic idea to a well-defined API and software architecture. This method combines different workshop formats, namely Domain Storytelling and Event Storming, to effectively define software systems collaboratively within cross-functional teams. The process starts with gathering business requirements using Domain Storytelling, a visual technique that provides a thorough understanding of business processes. A visual glossary then helps to create an unambiguous language for all stakeholders. The identification of bounded contexts, essential for defining independent and maintainable services, is done using Event Storming workshops. Once these contexts are identified, a context map is created to give a preliminary architectural overview and to guide the configuration of implementation teams. APIs are then formulated using OpenAPI and AsyncAPI, typically in a mob programming setting. This lean approach ensures faster time to market with committed stakeholder, business, and technical expert involvement.

This chapter explores the potential of games as a tool for innovation in the life sciences, examining their applications in education, research, and therapy, and highlighting the advantages and challenges of using games for scientific purposes. While traditional approaches to scientific inquiry have often been limited by issues such as access, motivation, and engagement, games offer a unique opportunity to address these challenges and unlock new avenues for scientific exploration. Through case studies and examples, this chapter demonstrates how games can be used to support scientific innovation in a variety of fields, including medicine, genetics, neuroscience, and ecology. However, the chapter also acknowledges some of the challenges and limitations of using games in the life sciences and discusses how these challenges might be addressed in the future. Ultimately, the chapter argues that games have the potential to revolutionise scientific research and innovation in the life sciences and provides recommendations for future research and development in this exciting and rapidly evolving field.

This chapter delves into digitalization in the life science industries, framing it not only as a technological transformation but also as a significant organizational change initiative. Digitalization’s success lies in managing expectations, driving change, and engaging users in adopting new solutions and work methods. It challenges the perception of digitalization as a one-time event, highlighting the need for continuous improvement to meet evolving requirements and support flexible operations like agile manufacturing. It further emphasizes the importance of understanding the limited capacity for users to absorb change while maximizing the development of digital solutions, necessitating strategic roadmapping and release management. Lastly, the chapter underscores the role of multiple digital solutions in users’ workflows and the improbability of a single platform fulfilling all tasks. A user- and process-centric mapping approach, similar to customer journey mapping, can help identify pain points and opportunities to improve workflow transitions and enhance the overall employee experience. The chapter is enriched with various case studies, providing practical insights for managing digital transformation in life science organizations effectively.

Building an open innovation healthcare and life science ecosystem platform can involve a local or global approach (Chesbrough et al., California Management Review 56:144–171, 2014). Through knowledge transfer (Bacon et al., International Journal of Information Management 49:377–387, 2019), inter-organizational relationships (Radziwon & Bogers, Technological Forecasting and Social Change 146:573–587, 2019), research opportunities (West & Bogers, Innovation 19:43–50, 2017), effects on entrepreneurship (Nambisan et al., Strategic Entrepreneurship Journal 12:354–368, 2018), and connecting corporations and communities (Gupta et al., Journal of Open Innovation: Technology, Market, and Complexity 3:17, 2017), there could be an amazing potential for open innovation healthcare ecosystem platforms.

The research question of Study No. 1 was motivated by open innovation platforms in other industries—such as Local Motors in the automotive industry—where technology products have been built with an effective marketing approach that attracts creators and innovators. As we first researched “Academics’ willingness to participate, collaborate and co-create on an open innovation ecosystem platform, to develop new medical technology products with other individuals, groups, communities and corporations” now the follow-up study (Study No. 2) focuses on “Academics’ willingness to participate in Open Innovation Healthcare Ecosystem Platform outside their qualifications and working experience,” which would also lead to the fact that they have to build a certain autodidactic competence. This should also activate their creativity and new individual and group approaches to innovation management. So we asked just those who answered in Study No. 1 their willingness to participate in an Open Innovation Ecosystems Platform Project to develop Medical Technology Products.