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 Table of Contents  
Year : 2022  |  Volume : 8  |  Issue : 1  |  Page : 1

The UpSMART Accelerator: driving digital innovation to change the conduct of early phase cancer medicine trials

1 Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, United Kingdom
2 Fondazione IRCCS Istituto Nazionale dei Tumori di Milano, Milan, Italy
3 Research Coordination Area, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Barcelona Hospital Campus, C/ Natzaret, 115-117, 08035 Barcelona, Spain
4 The Christie NHS Foundation Trust, Manchester; Division of Cancer Sciences, University of Manchester, United Kingdom
5 Cancer Research UK, London, United Kingdom
6 Fondazione IRCCS Istituto Nazionale dei Tumori di Milano; Università Statale di Milano, Milan, Italy
7 Medical Oncology – Oncology Data Science (ODysSey) Group, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, C/ Natzaret, 115-117, 08035 Barcelona, Spain
8 Medical Oncology Department, Vall d'Hebron Institute of Oncology (VHIO), Vall d'Hebron Hospital Universitari, Vall d'Hebron Barcelona Hospital Campus, C/ Natzaret, 115-117, 08035 Barcelona, Spain
9 Microsoft Corporation, Berkshire, United Kingdom
10 Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester; Rekaryo Heath Ltd, West Midlands, United Kingdom
11 The Christie NHS Foundation Trust, Manchester, United Kingdom
12 Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester; Division of Cancer Sciences, University of Manchester, United Kingdom

Date of Submission23-Feb-2021
Date of Decision01-Jun-2021
Date of Acceptance09-Jun-2021
Date of Web Publication25-Jan-2022

Correspondence Address:
Butt Fouziah
digital Experimental Cancer Medicine Team, Cancer Research UK Manchester Institute Cancer Biomarker Centre, University of Manchester, Alderley Park, SK10 4TG
United Kingdom
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Source of Support: None, Conflict of Interest: None

DOI: 10.4103/digm.digm_3_21

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Digitalizing clinical trials provide an opportunity to address challenges faced in the Phase I trial settings, where near real-time data capture and data interpretation are prerequisites for iterative decision-making to rapidly adapt trial designs based on emerging insights. Although digital technologies have driven significant improvements in many businesses and organizations, the adoption of digital technologies in clinical trials has been slow. In recognition of this lag, the UpSMART consortium, a 5-year funded program (2020–2024), has been established in Europe between the UK, Spain, and Italy to embrace digital technologies and drive benefits to patients. The consortium, led by the Cancer Research UK Manchester Institute Cancer Biomarker Centre, aims to 'digitalize' Experimental Cancer Medicine Centres in the UK and Early Drug Development Units in Spain and Italy by open-sourcing and sharing digital healthcare products between participating centers across the consortium. The goal is to optimize data capture and interpretation thus accelerating Phase I clinical research to ultimately benefit patients by allowing faster access to tomorrow's medicines.

Keywords: Cancer clinical trials, Digital healthcare products, Digitalizing

How to cite this article:
Fouziah B, Laura S, Luca A, Xenia VA, Louise C, Rachel C, Filippo DB, Silvia D, Rodrigo D, Elisabetta F, Elena G, Donna M G, Andrew G, Dónal L, Paul O, Akshita P, Aoife R, Jennifer K R, Julie S, Alison W, Caroline D, Andrew H. The UpSMART Accelerator: driving digital innovation to change the conduct of early phase cancer medicine trials. Digit Med 2022;8:1

How to cite this URL:
Fouziah B, Laura S, Luca A, Xenia VA, Louise C, Rachel C, Filippo DB, Silvia D, Rodrigo D, Elisabetta F, Elena G, Donna M G, Andrew G, Dónal L, Paul O, Akshita P, Aoife R, Jennifer K R, Julie S, Alison W, Caroline D, Andrew H. The UpSMART Accelerator: driving digital innovation to change the conduct of early phase cancer medicine trials. Digit Med [serial online] 2022 [cited 2023 Mar 24];8:1. Available from: http://www.digitmedicine.com/text.asp?2022/8/1/1/336592

  Introduction Top

Clinical drug development is time-consuming, complex and faced with challenges, and has high attrition rates for investigational medicinal products (IMPs).[1],[2] Issues including patient recruitment, adherence and retention, and data collection, sharing, and interpretation, can lead to unpredictable timelines and escalating costs.[3]

Successful development of anti-cancer therapies is dependent upon outcomes of early phase clinical trials. The initial phase of clinical trials, Phase I trials, are the first-in-human studies with the primary objective of characterizing the safety of the IMP. They form the basis of key decisions such as identification of the population that has the best predicted benefit-risk balance, and selection of the optimal dosing regimen for later research. These trials, therefore, present the drug development process with an early risk of failure if these key decisions are made incorrectly. Early phase studies often require intensive patient monitoring, multiple sampling procedures, and extensive data collection – all of which drive the adaptive decision-making that is required for optimal outcomes.[4]

The current practice of data collection relies on intermittent, sporadic sampling and typically paper-based collection methods. These approaches are nongranular and prone to transcription errors. Such a paper-based approach and lack of standardization in data collection can result in major issues with data accuracy, timeliness, and quality, with potential for “loss in translation” between preclinical and clinical observations resulting in misinterpretation of data affecting robust decision-making.[5],[6] Our goal is to optimize data capture and interpretation thus accelerating Phase I clinical research.

  Digitalizing Clinical Trials Top

Digital technologies have advanced business efficiencies dramatically over the last 15–20 years and are now making an impact in the healthcare sector.[7],[8] Individuals are leveraging technologies such as wearable devices and mobile technologies to monitor their own health and daily activity,[9] yet the adoption of digital technologies in clinical trials has been slow. Reluctance to innovate and adopt digital technologies in clinical trials may in part come from regulatory and privacy concerns.[10] However, regulators are making efforts to address these concerns and increase confidence in the use of digital technologies. Regulators, including the USA Food and Drug Administration (FDA) have promoted the use of digital technologies in the drug development process and issued guidance on their use: the FDA recently published the Digital Health Innovation Action Plan.[11],[12] In addition, broad-reaching and high-profile initiatives to accelerate the implementation of digital technologies in clinical trials, such as the Clinical Trials Transformation Initiative (CCTI),[13] National Institutes of Health (NIH) and National Science Foundation (NSF),[14] and the SMART and Connected Health Program (NSF-18-541),[1] have been initiated to generate improvements in clinical trial efficiency and data quality.

We define digital healthcare products (DHPs), as “digital technologies, medical devices or software (including machine learning) used for the purposes of supporting/enabling clinical decision making” [Appendix 1]. The use of DHPs to digitalize clinical trials holds promise to the key stakeholders of any clinical trial (patients, investigators, and sponsors) and to all stages of the clinical trial process, namely design, initiation, recruitment, remote monitoring, data management, analysis, and reporting.[10],[15],[16],[17]{INLINE 1}

DHPs in the Phase 1 setting could help improve data capture by continuous monitoring through the use of connected devices and wearables. Automation of the data collection and transfer process could reduce transcription errors and missing data points, producing consistent accurate measurements, integration and visualization of different data types and enabling the rapid sharing of emerging data, thereby improving interpretation.[18] In addition, DHPs could assist with the challenges presented by real-time data interpretation of high volume and complex adverse reactions, efficacy measures, and the patients' general wellbeing.[19] In particular, the incorporation of artificial intelligence/machine learning with improved data analytics and decision support tools could augment data interpretation.[20] To supplement these, algorithms are starting to be used to assist with trial planning, predictive monitoring, and outcomes.[13],[14],[21],[22] Improvements in automation, volume, and coverage of dynamic data gathering are also likely to provide a more accurate and comprehensive picture of an individual's health than the traditional visit-based “snapshots” which are often subject to recall bias. The deployment of DHPs will allow data to be captured off-site, resulting in reduced hospital visits and potentially improved patient adherence, moving towards the much-desired “de-centralized clinical trial.”[20],[23] For these reasons, and a potential overall cost-benefit, sponsors, and investigators are beginning to consider digital health technologies at an early stage of the drug development process.[12],[20]

Patient assessments at hospitals have recently been impacted by COVID-19. In this setting, digital technologies proved invaluable for continued and remote monitoring to assess patient health.[24] This demonstrates that when a key clinical need is apparent, hospitals can overcome large cultural barriers to digital technology implementation driven by continued care for their patients. These practice changes have catalyzed Trusts and organizations in the clinical trials sector to reflect on the potential value to be gained by changing traditional approaches and instead adopting digital technologies, although still presented with regulatory challenges and protocol amendment approvals.[25]

Recognition of an apparent gap in the application of digital technology to Phase I cancer clinical trials presented an opportunity to create the UpSMART consortium. We describe how the UpSMART consortium was established to address the challenge of “digitalizing” cancer clinical trials, particularly Phase I.

The UpSMART Accelerator Program

The UpSMART consortium has been established between the UK, Spain, and Italy and consists of 23 “participating centers” across Experimental Cancer Medicine Centres in the UK and Early Drug Development Units in Spain and Italy (collectively known as experimental cancer centers). UpSMART creates a European network of centers to work collaboratively with the right expertize to rapidly enhance each center's digital capability, optimizing both data acquisition and interpretation, ultimately benefitting the patient. The consortium is funded by an Accelerator Award through a partnership between Cancer Research UK (CRUK), Associazione Italiana per la Ricerca Contro il Concro, and Fundación Científica de la Asociación Española Contra el Cáncer. In addition, the consortium welcomes “collaborating centers,” who although not funded directly via the Accelerator Award can collaborate, share, and receive information in the same way as the “participating centers” [Figure 1].{Figure 1}

  The Ambition Top

The ambition of the consortium is to accelerate the effectiveness and efficiency of early cancer medicine trials, (Phase I and non-randomized Phase II clinical trials) by improving data acquisition and interpretation. The consortium will drive digital innovation through the development and deployment of DHPs across the network for use in clinical trials of IMPs. With the more widespread use of digital technologies within experimental cancer centers across Europe, it is envisaged that better insights into how experimental medicine is performing in these early clinical trials can be achieved through more comprehensive, accurate, and interrogatable data. The ambition extends to:

  • Enhancing DHPs which are currently in use by a single-center allowing improved multi-center utility.
  • Developing new DHPs using the Accelerator Award funds, based on insights gained from “participating centers” in identifying the gaps in data acquisition and interpretation within the existing clinical trial process.

The overall goal is to release DHPs to the early clinical trials community which are free to use and ideally “open-source” such that the software code can evolve and improve through community use [Appendix 1]. The network will also give visibility to commercially available DHPs which are in use across the centers, further supporting the ambition. This visibility will allow individual centers to consider implementing commercially available DHPs from local funds based on actual end-user experience in another center, further enhancing the digitalization capabilities of individual centers.

  The Vision Top

The UpSMART consortium has a concerted vision - that driving the use of DHPs into early cancer medicine trials can help the sponsor and investigator to:

  • Improve data acquisition by providing centers with a digital infrastructure for collecting data.
  • Accelerate iterative data interpretation by more effective data integration and visualization.

This can be done in near real-time and by the application of artificial intelligence and machine-learning algorithms to the vast amounts of data collected, with the goal of providing insights otherwise overlooked by the human eye. We also hope to provide a better patient experience by (1) allowing data to be collected continuously to optimize patient safety and enable more informed decisions due to greater data collection, and (2) reducing the need to attend hospital for per-protocol assessments. This will be achieved through incorporating Patient and Public Involvement and Engagement into the proposed governance structures described below.

The vision extends to enabling centers across the network to be recognized as world leaders in conducting clinical trials of DHPs. This will be achieved by training the centers to become experts in understanding the regulations pertaining to medical devices, overall increasing the attractiveness of these centers to sponsors, and supporting successful funding applications for further research.

  The Program Top

A strategic program with key foundations of collaboration, program design, delivery, and coordination, is required to drive the adoption of DHPs into early phase cancer medicine trials.

  Collaboration Top

Digitalization of clinical trials can be time-consuming and costly; working in silos can take years to understand the digital landscape, to implement DHPs into clinical trials, and to establish expertize. With this in mind, the UpSMART consortium has taken a collaborative approach and assembled a team which brings expertize in designing, delivering, and interpreting data from early cancer medicine trials. The collaboration will remove competition between centers and provide transparency for those DHPs under development within any given “participating center” or “collaborating center” to others within the network. A key benefit will be the removal of duplicated effort, achieved through sharing experiences and benefits of any given DHP, which will facilitate the faster adoption of DHPs across the network.

  Program Design Top

The UpSMART program has been designed with an infrastructure to support the ambition and vision of the consortium and is implemented through four work packages (WPs). Each WP brings with it an expert leader(s) dedicated to enabling delivery of the WP outputs. Whilst each lead will be responsible for driving forward specific areas of the research, they will also work in collaboration as the interdependencies between WPs are central to overall success. The leadership for each of the WPs has been drawn from different backgrounds-clinical, academic, and technical, to ensure the correct expertize is leveraged into the program to make it a success. Digitalization also brings with it a requirement to create new roles within the clinical trial workforce, namely the “Clinical Trial Informatician,” dedicated to the application of DHPs into clinical trials. The “Clinical Trial Informatician” will ensure that:

  • Clinical research staff are trained in the application of DHPs.
  • DHPs are deployed according to national frameworks for their use.
  • The DHP can be reliably accessible to clinical research staff.

WP1 and WP2 are led by clinicians, who will assess and prioritize the DHPs identified across the network for clinical utility and impact on data acquisition (WP1) and data interpretation (WP2), respectively. Each clinical lead will be responsible for evaluating the identified DHPs relevant to their WP, prioritization of development of new DHPs, and enhancements to existing ones.

WP1 focuses on DHPs which will remove pen-and-paper techniques of collecting per-protocol assessments when patients attend their clinical research facility. WP1 will also investigate de-centralizing per-protocol assessments to the home, with an ultimate benefit to the patient and sponsor. WP2 will focus on identifying safety trends of concern and identify which clinical treatment will most benefit a patient through the integration of molecular and clinical data. In addition, WP2 will evaluate DHPs to determine those that allow for the best integration of data across sites and for patients to have access to their own data.

As described above, the UpSMART vision is for centers across the network to be classed as leaders in Europe in conducting clinical trials of DHPs; this will be driven through WP3. Conducting clinical trials in cancer patients where the primary endpoint is to characterize the performance characteristics of DHPs is an area which lacks knowledge and expertize. Educating centers and developing effective training programs is critical to the success of WP1 and WP2, and thus the interdependency on WP3. WP3 is led by academic and clinical leads who will address the required regulations and training programs needed to develop expertize in the clinical research team. In particular, a regulatory framework and roadmap to facilitate clinical trials of DHPs is currently in development by the digital Experimental Cancer Medicine Team (digital ECMT) at CRUK Manchester Institute, with the intent of an open-source release in 2021. This will enable research staff to use this roadmap to conduct clinical trials of DHPs to the required regulatory standards whilst developing expertize across Europe as an exemplar to centers outside the consortium.

Unlocking the potential of identified DHPs requires technical and software engineering expertize which will be provided through WP4. WP4 will look to currently published standards to develop a framework with minimal defined viable criteria to assess the maturity of DHPs available across the network. These criteria will allow each in-house DHP currently in use or development to be assigned to a categorization, single-center (S), multi-center (M) or universal (U) intended use, where S = simplest technical maturity and U = comprehensive technical maturity [Table 1]. This categorization of existing DHPs will enable each of the research centers to understand in which context the open-source DHP is best used and to establish its suitability for their intended use in an early cancer medicine trial, based on its current maturity status (whether its intended use is within a single-center, multi-center or universal). WP1 and WP2 have an interdependency on this WP as there is a reliance on WP4 to:{Table 1}

  • Develop the maturity status of a DHP if there is the priority from centers across the network in extending the use of a particular DHP.
  • Develop new DHPs as prioritized by the steering board.
  • Provide an open-source platform across the network to host the DHPs of interest.

  Delivery Top

For the program to be successful there needs to be an effective method for delivery across the network. The UpSMART program has taken a non-hierarchical model with a “coordinating center” enabling the “participating centers.” The “coordinating center” comprises the Manchester Cancer Research Centre, which consists of The Christie NHS Foundation Trust (leveraging leads from the clinical environment for WP1, WP2, and WP3) with CRUK Manchester Institute at the University of Manchester (home to the digital ECMT and providing the second WP3 lead and WP4 lead). In addition, the “coordinating center” provides the project and program administration, along with providing core software engineering capability for the program through the digital ECMT. “Participating centers” have committed to the program by (1) agreeing to consider beta testing the available DHPs and (2) sharing, through open-sourcing, any new DHPs developed within their centers. It is envisaged that each center within this model will use their existing research facilities, environment, and infrastructure to support their contribution to the program.

  Coordination Top

Finally, there is a need for the program to be coordinated through appropriate governance. There are four components of the UpSMART governance:

  1. Operations board: Responsible for the day-to-day delivery of the individual WPs.
  2. Digital review board: Responsible for the review of DHPs which are within the scope and assess the maturity of products for open-source release.
  3. International scientific advisory board: Responsible for the review of program outputs.
  4. Steering board: Accountable for reviewing and monitoring the strategic direction of the award and to make recommendations to the delivery team.

  Measuring Success Top

We have aligned the program to 15 key metrics which will measure the outputs from the program to establish if it is successful over the 5 years [Table 2]. The aligned metrics are tangible deliverables and will clearly identify if the program is delivering its goals, with annual reviews to track progress. To apply the metrics over the 5-year period there is first a requirement to understand the baseline metrics at the start of the program, assessed in the 1st year.{Table 2}

The focus of year 1 is to gather information across the network to baseline activities and is currently underway. The baseline data are being generated by two key activities. First, completion of an information gathering questionnaire by each of the DHPs “participating centers” and “collaborating centers”. Secondly, by conducting site visit interviews (remotely due to the impact of COVID-19) with each of the centers across the network to further understand the information presented from each center and identifying gaps for the development of new DHPs. These key activities will:

  1. Identify current levels of activity across the centers and how many DHPs in scope of the program have been/are being developed in-house.
  2. Allow an assessment of the maturity status of each in-house DHP within scope, aligning it to the framework provided by WP4 to categorize the product as a S, M, or U product [Table 1].
  3. Identify which, if any, in-house products are currently open-sourced and available for use.
  4. Identify any commercially available DHP used across centers to better enable data acquisition or interpretation.
  5. Identify centers across the network that are conducting clinical trials of DHPs and if so, establish each center's practice with regard to training and meeting regulatory requirements.
  6. Identify common themes across the network where there is a need for digitalization to improve clinical trial activities, with a focus on data acquisition and interpretation.
  7. Identify software engineering capability and resource at each center to assist with implementation of DHPs when open-sourced released, to enhance current in-house DHPs and development of new ones.

  Scientific And Strategic Impact Of The Program Top

The UpSMART consortium envisages that its outputs will address how to digitally acquire and rapidly interpret data to support more data-based decisions. This is achieved through eight key research questions across the four WPs [Table 3]. In addition, the strategic model of the program will address how to better train centers in conducting clinical trials of DHPs and create centers of excellence for the wider community to leverage expertize and knowledge in conducting clinical trials of DHPs. It will also show how embedding technical expertize within the design of the program can impact timelines to rapidly deliver DHPs across the network, swiftly digitalizing centers and thus fulfilling the consortium's ambition.{Table 3}

  Conclusion Top

We have outlined some important challenges that must be overcome to accelerate the drug development process, with a focus on early cancer medicine trials, where challenges around data acquisition and data interpretation need to be addressed. In doing so, we recognized that digitalization of early clinical trials promises a significant opportunity to transform trial conduct and processes, expediting clinical research, and potentially improving the clinical translatability of preclinical observations by improving the sensitivity, specificity, and integration of the complex data obtained. The recent COVID-19 pandemic has demonstrated the need for, and impact of digital tools including remote patient consultations. We have seen how embracing the changes made possible by technology have proved critical in allowing patient health to be monitored during this pandemic. The uptake of digitalization into clinical operations has the potential to positively impact investigator, patient and sponsor workflows, improving efficiencies and data quality and most importantly, benefitting patient outcomes.

Recognizing the clear potential of digitalization, we have taken the initiative through the UpSMART consortium to facilitate the digitalization of early cancer medicine trials across Europe. This consortium seeks to drive digital innovation, empowering European centers to enhance their attractiveness to sponsors looking to place their Phase I trials. By fostering a collaborative approach, we combine expertize in early cancer medicine trials across Europe with a concerted vision to unlock the potential of digitalization and drive DHPs into early cancer medicine trials, assessing how the application of these products can impact data acquisition and interpretation. The program's 1st year objectives are well underway; baseline metrics are being established and DHPs are being evaluated for potential value to the network.

DHPs developed by the UpSMART consortium will be freely shared with the clinical research community through open-sourcing these DHPs. Insights will also be shared through publications to address whether digital data capture can effectively reduce both false positives and negatives when translating from clinical to preclinical observations and whether digital data capture combined with DHPs can assist data interpretation. Allowing better visualization of multi-modality data, and alerts to safety signals using algorithm-based analysis, will change the way in which data is interpreted and allow rapid iterative decision-making.

The patient is at the heart of the UpSMART consortium's ambition and vision and there is a desire to provide a better, safer patient experience on trials through the implementation and use of DHPs. Thus, it is envisaged enabling better data acquisition and interpretation from early cancer medicine trials will allow faster, smarter, iterative data-based decision-making to accelerate the drug development process and ultimately benefit the patient by providing access to promising treatments sooner.


We gratefully acknowledge all the participants in the UpSMART Accelerator Consortium for use of the digital healthcare products available across the Consortium. We would like to thank Ekram Aidaros-Talbot and Jennifer Ward (Cancer Research UK Manchester Institute) for their contribution to operational activities to facilitate the running of the program, and Andrew Porter (Cancer Research UK Manchester Institute) for a pre-submission review of the manuscript. The Vall d'Hebron Institute of Oncology (VHIO) would like to thank the Welfare Projects Division of the 'La Caixa' Foundation to the Molecular Therapeutics Research Unit (UITM) for their support.

Financial support and sponsorship

This work was supported by Cancer Research UK (CRUK), Associazione Italiana per la Ricerca sul Cancro (AIRC) and Fundacion Científica – Asociacion Espanola Contra el Cancer (FC -AECC), via an Accelerator Award [A29374] through the CRUK Manchester Institute [C147/A25254].

Conflicts of interest

RD: advisory role for Roche and Boehringer Ingelheim; speaker's fee from Roche, Ipsen, Amgen, Servier, Sanofi, and Merck Sharp & Dohme; research grants from Merck and Pierre Fabre. EG: research funding from Novartis, Roche, Thermo Fisher, AstraZeneca, Taiho, and BeiGene; travel grants from Bristol-Myers Squibb; Merck Sharp & Dohme; Menarini, and Glycotope; advisory roles for Roche/Genentech, F. Hoffmann La Roche, Ellipses Pharma, Neomed Therapeutics1 Inc, Boehringer Ingelheim, Janssen Global Services, SeaGen, TFS, Alkermes, Thermo Fisher, Bristol-Myers Squibb, MabDiscovery, and Anaveon; speakers' bureau for Merck Sharp & Dohme, Roche, Thermo Fisher and Lilly; principal or co-principal investigator for Agios Pharmaceuticals, Amgen, Bayer, Beigene, Blueprint Medicines, BMS, Cellestia Biotech, Debiopharm, F. Hoffmann La Roche, Forma Therapeutics, Genentech Inc, Genmab B.V. GlaxoSmithKline, Glycotope Gmbh, Incyte Biosciences, Incyte Corporation, ICO, Kura Oncology Inc, Lilly SA, Loxo Oncology Inc, Macrogenics Inc, Menarini Ricerche Spa, Merck, Sharp & Dohme de España SA, Nanobiotix SA, Novartis Farmacéutica SA, Pfizer SLU, Pharma Mar SAU, Pierre Fabre Medicament, Principia Biopharma Inc, Psioxus Therapeutics Ltd, Sanofi, Sierra Oncology Inc, Sotio AS, and Symphogen A/S. CD: research funding from AstraZeneca, Astex Pharmaceuticals, Bioven, Amgen, Carrick Therapeutics, Merck AG, Taiho Oncology, GlaxoSmithKline, Bayer, Boehringer Ingelheim, Roche, BMS, Novartis, Celgene, Epigene Therapeutics Inc, Angle PLC, Menarini, Clearbridge Biomedics, Thermo Fisher Scientific, and Neomed Therapeutics; honoraria for consultancy/advisory boards from Biocartis, Merck and AstraZeneca.

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  In this article
Digitalizing Cli...
The Ambition
The Vision
The Program
Program Design
Measuring Success
Scientific And S...

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