Earlier this year, Scimcon announced the launch of a new Graduate Recruitment Programme, aiming to attract new talent to our team. We’ve partnered with Sanctuary Graduates, a recruitment agency specialising in sourcing talented graduates for suitable roles within a variety of industries.
Joscelin Smith is one of our newest recruits, and Scimcon’s first graduate consultant to join us through the programme. We sat down with Joscelin to discuss her background, what led her to Scimcon, and what her experience has been like as a graduate joining the Scimcon team.
Science has always been a passion of mine, so after studying Biochemistry at Bristol University, I went on to work as a Research Assistant at Cambridge University, where I focused on Immunology. I then travelled to Auckland, to complete my PhD on the cardiac nervous system.
It was during this time that I started experimenting with software and coding, which really piqued my interest. This shifted my career trajectory towards a role that incorporated both science and technology, which is of course something I’ve been able to explore working at Scimcon.
I had a good idea of the type of role I was after, so after talking to and sending my CV to Sanctuary Graduates, the team put me in touch with Scimcon, who really matched what I was looking for. The interview was quickly set up, and the whole process was very smooth and painless, with a frequent channel of dialogue and updates from the Sanctuary end.
As a Graduate Information Systems Consultant, a large part of my role is helping clients implement various systems and software, such as SDMS and LIMS. I also help clients to problem-solve and alleviate any issues they are having with this process. I have been working in this role for around 6 months, which has mostly been a training period so far, shadowing multiple people across various roles. This has included working with Geoff, Scimcon’s Co-Founder and Principle Consultant, on a digital transformation strategy day, during the early stages of our work with a new client. I found this fascinating as it showed me how Scimcon can add real strategic value to clients. I have also worked with our Informatics Project Manager Lynda Weller, as well as Jon Fielding – one of the Project leads here at Scimcon. Being able to work with different colleagues has been very interesting and provided extremely useful insights into the role, as well as Scimcon in general.
The prospect of problem-solving first attracted me to this role, and being involved in the resolution of a particular issue for a client has been really rewarding so far. I didn’t know exactly what to expect but the project management has also emerged as a really enjoyable aspect of the job. Having worked in the lab myself, I really see the value in Scimcon’s mission to help make laboratory workflows more efficient.
As I’m familiar with a lot of the systems we work on, I can translate my experience in the lab to my role at Scimcon, working on design and implementation.
I am finding it incredibly fulfilling working for a company which is trying to bridge that gap and give more time back to scientists. I believe this process is invaluable and is something I am proud to be working on.
My previous lab experience was helpful to evaluate different career paths, and ultimately I am pleased that it has led me to my role as a Graduate Information Systems Consultant for Scimcon. I am really looking forward to advancing my career within the company and in the short term I am hoping to gain more exposure to different projects and the different systems we work with.
To read more about how Sanctuary Graduates are helping to provide Scimcon with talented candidates to add to our expertise in data informatics, read our previous blog.Industry leader interviews: Pascale Charbonnel?
Our latest industry leader interview is with Pascale Charbonnel, who tells us about how SCTbio supports customers through the cell therapy manufacturing chain.
In this instalment of our industry leader series, we speak to Pascale Charbonnel, Chief Business Officer of SCTbio. Pascale tells us about the work of SCTbio, how they collaborate with biotech developers, and why they are a great choice for outsourcing cell and gene therapy (CGT) manufacture.
SCTbio is a cell-based therapy and viral vector contract development and manufacturing organisation (CDMO). Originally part of the SOTIO group, we spun out in 2022 and operate a Good Manufacturing Practice (GMP) facility in Prague, Czech Republic. Recently, eureKING, a French special purpose acquisition company, or SPAC, has signed an agreement to purchase full ownership interest in SCTbio, which will further bolster our position as a leading CDMO service provider.
As part of SOTIO group, we were developing our own cell and gene therapies for 13 years, so we have a lot of experience in manufacturing for clinical trials from phase I to phase III across multiple geographies. Given this expertise, customers trust us to guide them through the development process as they navigate the GMP world and clinical development.
Our target customers are mainly early-stage biotechnology companies, who typically outsource all their production needs. We are sometimes also used as an additional facility to absorb around 20-30% of the production needs for large Ph II / Ph III phases. Our main goal is to establish trust with customers right from the beginning, so we can then support them as the project progresses through later clinical phases. The average customer project takes about two years.
With our history in SOTIO, we can ensure GMP compliance for the full drug development life cycle as we have also faced some of those same hurdles associated with developing therapeutics. Our team understands the importance of saving time and costs, and maintaining momentum to ensure approvals run smoothly and that we can move onto the next clinical stage. We use this experience to create optimised development plans, which give customers the assurance that we can support them and hopefully go on this journey with them for many years to come.
We are still very much in a mixed model – so we have turned to electronic systems in some cases, but we do still have paper-based approaches too. It’s useful to have both, as it means we can tailor our approach depending on customer requirements. We’ve built our own data management system, which has been developed specifically to fit our operation here – so while there is scope for us to move to a full digital system, it will take time and our customers’ current requirements do not warrant that.
When it comes to customer data, we typically start by storing the raw data in a validated platform which we can then manage regularly. We then export it to the customer in whatever format they wish. As each customer’s requirements differ greatly, there’s no need for us to move to full digital systems yet, but it’s definitely something we’re bearing in mind for the future.
Since last year, we’ve run four audits – three by customers, one by a regulatory body. They all follow a similar process, where we will receive a request or announcement about two weeks in advance that an auditor is going to visit, and they usually request specific documentation which of course we already have to hand. During the day they will look at everything in our facility, speak to some of our technical staff, and then make a report outlining any observations.
GMP culture is very deeply rooted in our company, to the point where our recent regulatory audit returned no observations at all! While this shows everything was as expected, our customers were particularly impressed. One of our customers came back to us following their audit to say that they can see we go above and beyond the standard for GMP, and that our team is clearly well organised and collaborative.
One thing I think really makes us special is our people. We are a team of about 80 people, many of whom have been with us since the inception of SOTIO, and the staff turnover rate is very low indeed. It gives our customers a great deal of assurance that as well as having far-reaching experience in developing drugs and a deeply rooted GMP culture, our people are committed to our customers and get to know them and their needs.
What set us apart is our 13 years expertise in the CGT field and our flexibility to accommodate different sizes/stage of projects. We plan to stay very flexible, so that we can continue to take a bespoke approach to supporting our customers.
In addition, we offer a really wide range of services. We can collect the starting material, process it in our facility, release it under quality assurance / qualified person (QP/QA) and GMP conditions, and we have a logistical advantage as we’re based in central Europe, so close to a number of key markets. Being able to offer a full start-to-finish process in one place is quite unusual, so it gives us a strong advantage.
The recipe for success as a CDMO in my eyes is to have mutual trust and transparent communication with partners and customers, so with highly skilled people and low turnover, as well as the cost benefits of our location, our customers rely on us for consistency, reliability, and quality.
The market has faced many challenges over the last few years, but we’re now starting to see an upturn. Funding is becoming available again, and we believe that ‘the good science’ will prevail. We’re excited to see what projects will come our way and to keep supporting customers to develop life-changing medicines.
Scimcon is proud to showcase CDMOs like SCTbio, and we’re looking forward to seeing how the company will grow over the coming years. To contribute to our industry leader blog series, or to find out more about how Scimcon supports organisation with lab informatics and data management solutions, contact us today.
In this blog, we speak to Jana about Navignostics’ mission, and how the team plans to revolutionise personalised oncology treatments with the help of data and AI.
Navignostics is a start-up personalised cancer diagnostics business based in Zurich, Switzerland. Our goal is simple – we want to revolutionise cancer treatment by identifying a highly personalized and thus optimal treatment for every patient, to ensure that each patient’s specific cancer is targeted and fought as needed. Our capabilities allow us to do this by analysing tumour material, through extracting spatial single-cell proteomics information. and using this data to analyse many proteins simultaneously in individual cells within the tissue.
Single-cell proteomics comprises of measuring and identifying proteins within a single cell, whereas spatial proteomics focuses on the organisation and visualisation of these proteins within and across cells. Combining these two research tools allows the team at Navignostics to characterise tumours on a cellular level, by identifying the proteins present across cells in a tumour, and also how these proteins and cells are organised. This means that the team can provide a more accurate estimate for how certain tumours will respond to different medications and treatments.
Proteins are typically the target of cancer drugs and measuring them on a cellular level allows us to identify different types of tumour cells, as well as immune cells that are present and how the two interact. This data is highly relevant to inform clinicians of the best form of (immuno-) oncology and combinatorial treatment for individual patients. Also, this information is highly relevant to pharma companies in order to accelerate their oncology drug development, by providing insight on drug mode of action, and signatures to identify responders to novel drugs.
The kind of data that we are able to extract from different types of tumours are monumentally valuable, so the work doesn’t stop there. All of the data we harness from these tumours is stored centrally, and we plan on utilising this data by building it into a system we refer to as the Digital Tumour, that will continuously allow us to improve the recommendations we can make to our clinical and pharma partners. Our journey has been rapid, though it is built on years of research and preparation: we founded the business in 2022, as a spin-off from the Bodenmiller Lab at the University of Zurich.
The dream became a reality for us in November 2022, when we secured a seed investment of 7.5m CHF. This seed funding will allow us to pursue our initial goals of establishing the company, achieving certification for our first diagnostic product and developing our Digital Tumour. By extension, collaborating with pharma and biotech partners in oncology drug development. It has also given us the resource we need to move to our own premises. We are due to move off university campus in May 2023. This offers us great opportunity to push forward with the certification processes for our new lab, and it gives us to the chance to grow our team and expand our operation. We will be located in a start-up campus for life science organisations in the region of Zurich, so we’ll be surrounded by companies operating in a similar field and at a similar capacity.
The Digital Tumour will be the accumulation of all the molecular data we have extracted from every tumour that we have analysed to date, and ongoing. Connected to that, we store information on the clinical parameters and patient response to treatment. Over time, our aim is to utilize this central data repository to identify new tumour signatures, and build a self-learning system that will provide fully automated treatment suggestions for new patients, based on how their molecular properties compare to previously analysed patients that have been successfully treated.
Our data storage is quite advanced, so volume isn’t really a challenge for us. Our main focus is standardising the input of data itself. The technology is based on years of research and the data analysis requires a great deal of experience and in-depth expertise. In order to extract the full value from this data, it must be completely standardised. Data integrity is therefore vital to our work, and allows us to get the maximum value from past analyses. Our past experience in the Bodenmiller Lab allowed us to develop standardised processes to ensure that all of our data is fully comparable, which means that we can learn more and more from our past data, and apply this to new cases that we analyse.
It is also important to report on our complex data in a comprehensive but easily interpretable manner to the clinician/tumour board who needs to organise a treatment plan. We’re currently working with our clinical collaborators to develop readily understandable and concise reporting outputs. Unlike genomics analysis, our reports focus on proteins in tissue, which is the same information that clinicians are used to working with. So, there is a common language there that offers us the unique opportunity to provide clinicians with data they can easily interpret and work with.
It’s important to note that personalised treatment approaches and precision medicine are not new concepts in the diagnostics space. However, our technology and algorithms allow us to extract novel types of biomarkers which were previously inaccessible or unknown, so we’re helping to level up the playing field and give clinicians and drug developers’ comprehensive information to individualize therapies.
Comprehensive tumour data is truly at the heart of what we do, and one key benefit of our technology is that we’re able to analyse very small amounts of sample – such as fine needle biopsies – to provide therapy suggestions. We can also analyse bio banked tumour material, so if there is any old material that has been stored, we have the ability to analyse those samples retrospectively. Not only does this help us to fuel our Digital Tumour with more data, but it also allows us to examine new fields such as long-term survival rates of patients with these tumours. This is of huge value to fuel our product development pipeline because it allows us to identify different molecular properties between individuals that may not have been considered on a clinical level, but may have played a role in patient responses to treatments and survival outcomes in the long-term.
This kind of retrospective data also plays a key role in the evolution of healthcare and drug development, as having the technologies available to acquire this sort of data and mine it to our advantage will provide enormous benefits. These include improving individual treatment courses for patients, as well as expediting the development of novel cancer drugs so pharma companies can get more effective treatments to market sooner.
For example, one commonly cited statistic is that 90% of clinical drug development fails during phase I, II, III trials and drug approval. Often, this may arise from a lack of available information to identify the subset of patients most likely to benefit from a novel drug. Having access to Navignostics’ technology and algorithms and a database such as the Digital Tumour will offer the potential to pre-select the right patients to enroll in clinical trials, and more easily identify the patients that do respond to the novel treatment, which could substantially expedite the speed of drug development in the trial stage, and help bring more effective drugs to the market.
Even unsuccessful trials offer valuable opportunities: it is possible to repurpose and reanalyse material from previous failed trials. Such high rates of failure in clinical development means that there are a large number of companies that have invested $millions in developing drugs that have not come to fruition, so if companies want to re-mine their data, our team can reinterpret the existing work into identifying more successful strategies, so we can give those drugs another chance and offer a better chance of Return on Investment.
A failure no longer needs to be a failure. Navignostics and its offerings can bring value to our pharma and biotech partners, and will also bring direct benefit to patients and clinicians once we launch our diagnostics product. So, data from every facet of the oncology industry, from curing a patient to halting the development of a drug, can offer us valuable insight that both we and the Digital Tumour could learn from when developing treatments.
The next three years will be critical for our work, and we have projected timelines and key milestones for our diagnostics developments that we will achieve until our next funding round. Along the way, we are actively speaking to biotech and pharmaceutical organisations to identify projects and build the foundation for long lasting collaborations. We are looking forward to a successful continuation of the Navignostics development in 2023!
Scimcon is proud to showcase start-up companies like Navignostics, and we’re looking forward to seeing how the company will grow over the coming years.
To contribute to our industry leader blog series, or to find out more about how Scimcon supports organisation with lab informatics and data management solutions, contact us today.
Our team at Scimcon is made up of a talented group of interesting individuals – and our newest recruit Ben Poynter certainly does not disappoint!
Ben joined our Scimcon team in July 2022 as an associate consultant, and has been working with the lab informatics specialists to get up to speed on all things Scimcon. We spoke to Ben about his experience so far, his interests, background, and what he hopes to achieve during his career as an informatics consultant.
So, I studied Biomedical Science at Sheffield Hallam University, which was a four-year course and allowed me to specialise in neuroscience. During my time at university, I created abstracts that were presented in neuroscience conferences in America, which was a great opportunity for me to present what I was working on. My final year dissertation was on bioinformatics in neuroscience, as I was always interested in the informatics side of biomedical science as well.
Once COVID hit, I moved into code work, and worked in specimen processing, and then as a supervisor for PerkinElmer who were undertaking some of the virus research. When things started to die down, I began working for a group called Test and Travel (not the infamous Track and Trace initiative, but a similar idea!). I started there as a lab manager, training new staff on lab protocols for COVID-19, and then a month into that I started working more on the LIMS side – which is where I ended up staying. I wrote the UAT scripts for 3 different companies, I performed validation on the systems, I would process change controls. I then moved to Acacium as LIMS lead there, so over the course of my career I’ve worked with a number of LIMS and bioinformatics systems, including LabWare 7, LIMS X, Labcentre, WinPath Enterprise, and Nautilus (ThermoFisher Scientific).
In the early stages, I would have to say it was when Jon and Dave led my first interview, and Jon asked me a question I hadn’t been asked in an interview setting before. He asked me ‘who is Ben Poynter?’. The first time I answered, I discussed my degree, my professional experience with LIMS and other informatics systems, and how that would apply within Scimcon’s specialism in lab informatics consultancy. Then he asked me again and I realised he was really asking what my hobbies were, and how I enjoyed spending my free time. Since starting at Scimcon, I’ve been introduced to the full team and everyone is happy to sit and talk about your life both inside and outside of work, which makes for a really pleasant environment to work in. Also, it seems as though everyone has been here for decades – some of the team have even been here since Scimcon’s inception back in 2000, which shows that people enjoy their time enough to stay here.
I’ve been given a really warm welcome by everyone on the team, and it’s really nice to see that everyone not only enjoys their time here, but actively engages with every project that’s brought in. It’s all hands on deck!
So, my main hobbies and interests outside of work are game design, as well as gaming in general. I run a YouTube account with friends, and we enjoy gaming together after work and then recording the gameplay and uploading to YouTube. We are also working on a tower defence game at the moment, with the aim to move into more open world games using some of the new engines that are available for game development.
In addition to gaming and development, I also enjoy 3D printing. I have a 3D printer which allows me to design my own pieces and print them. It’s a bit noisy, so I can’t always have it running depending on what meetings I have booked in!
Technology is a real interest of mine, and I’m really fortunate to have a role where my personal interests cross-over into my career. The language I use for game design is similar to what I work with at Scimcon, and the language skills I’ve developed give me a fresh perspective on some of the coding we use.
At the moment, I’m working on configuration for some of the LIMS systems I’ll be working with at customer sites, which I really enjoy as it gives me the chance to work with the code and see what I can bring to the table with it. Other projects include forms for Sample Manager (ThermoFisher Scientific), making it look more interesting, moving between systems, and improving overall user experience. It’s really interesting being able to get to grips with the systems and make suggestions as to where improvements can be made.
My first week mainly consisted of shadowing other Scimcon lab informatics consultants to get me up to speed on things. I have been working with the team on the UK-EACL project, which has been going really well, and it’s been great to get that 1-2-1 experience with different members of the team, and I feel like we have a real rapport with each other. I’ve been motoring through my training plan quite quickly, so I’m really looking forward to seeing the different roles and projects I’ll be working on.
I’d really like to get to grips with the project management side of things, and also love to get to grips with the configuration side as well. It’s important to me that I can be an all-round consultant, who’s capable at both managing projects and configuration. No two projects are the same at Scimcon, so having the capability to support clients with all their needs, to be placed with a client and save them time and money, is something I’m keen to work towards.
For more information about Scimcon and how our dedicated teams can support on your lab informatics or other IS projects, contact us today.Top tips for best approaches to data use in clinical trials?
Maintaining data quality is critical in clinical trials. As a follow up to his first blog, we have worked with Industry Leader Mark Elsley to create this infographic, outlining Mark’s top tips for managing clinical trial data.
Mark Elsley is a Senior Clinical Research / Data Management Executive with 30 years’ experience working within the pharmaceutical sector worldwide for companies including IQVIA, Boehringer Ingelheim, Novo Nordisk and GSK Vaccines. His specialist area of expertise is in clinical data management, and he has published a book on this topic called A Guide to GCP for Clinical Data Management.
I am Mark Elsley, a Senior Clinical Research / Data Management Executive with 30 years’ experience working within the pharmaceutical sector worldwide for companies including IQVIA, Boehringer Ingelheim, Novo Nordisk and GSK Vaccines. I am skilled in leading multi-disciplinary teams on projects through full lifecycles to conduct a breadth of clinical studies including Real World Evidence (RWE) research. My specialist area of expertise is in clinical data management, and I have published a book on this topic called “A Guide to GCP for Clinical Data Management” which is published by Brookwood Global.
Data quality is a passion of mine and now receives a lot of focus from the regulators, especially since the updated requirements for source data in the latest revision of ICH-GCP. It is a concept which is often ill-understood, leading to organisations continuing to collect poor quality data whilst risking their potential rejection by the regulators.
White and Gonzalez1 created a data quality equation which I think is a really good definition: They suggested that Data Quality = Data Integrity + Data Management. Data integrity is made up of many components. In the new version of ICH-GCP it states that source data should be attributable, legible, contemporaneous, original, accurate, and complete. The Data Management part of the equation refers to the people who work with the data, the systems they use and the processes they follow. Put simply, staff working with clinical data must be qualified and trained on the systems and processes, processes must be clearly documented in SOPs and systems must be validated. Everyone working in clinical research must have a data focus… Data management is not just for data managers!
By adopting effective strategies to maximise data quality, the variability of the data are reduced. This means study teams will need to enrol fewer patients because of sufficient statistical power (which also has a knock-on impact on the cost of managing trials).2 Fewer participants also leads to quicker conclusions being drawn, which ultimately allows new therapies to reach patients sooner.
I believe that clinical trials data are vitally important. These assets are the sole attribute that regulators use to decide whether to approve a marketing authorization application or not, which ultimately allows us to improve patient outcomes by getting new, effective drugs to market faster. For a pharmaceutical company, the success of clinical trial data can influence the stock price and hence the value of a pharmaceutical company3 by billions of dollars. On average, positive trials will lead to a 9.4% increase while negative trials contribute to a 4.5% decrease. The cost of managing clinical trials amounts to a median cost per patient of US$ 41,4134 or US$ 69 per data point (based on 599 data points per patient).5. In short, clinical data have a huge impact on the economics of the pharmaceutical industry.
Healthcare organizations generate and use immense amounts of data, and use of good study data can go on to significantly reduce healthcare costs 6, 7. Capturing, sharing, and storing vast amounts of healthcare data and transactions, as well as the expeditious processing of big data tools, have transformed the healthcare industry by improving patient outcomes while reducing costs. Data quality is not just a nice-to-have – the prioritization of high-quality data should be the emphasis for any healthcare organization.
However, when data quality is not seen as a top priority in health organizations, subsequently large negative impacts can be seen. For example, Public Health England recently reported that nearly 16,000 coronavirus cases went unreported in England. When outputs such as this are unreliable, guesswork and risk in decision making are heightened. This exemplifies that the better the data quality, the more confidence users will have in the outputs they produce, lowering risk in the outcomes, and increasing efficiency.
ICH-GCP8 for interventional studies and GPP9 for non-interventional studies contain many requirements with respect to clinical data so a thorough understanding of those is essential. It is impossible to achieve 100% data quality so a risk-based approach will help you decide which areas to focus on. The most important data in a clinical trial are patient safety and primary end point data so the study team should consider the risks to these data in detail. For example, for adverse event data, one of the risks to consider could include the recall period of the patient if they visit the site infrequently. A patient is unlikely to have a detailed recollection of a minor event that happened a month ago. Collection of symptoms via an electronic diary could significantly decrease the risk and improve the data quality in this example. Risks should be routinely reviewed and updated as needed. By following the guidelines and adopting a risk-based approach to data collection and management, you can be sure that analysis of the key parameters of the study is robust and trust-worthy.
Aside from the risk-based approach which I mentioned before, another area which I feel is important is to only collect the data you need; anything more is a waste of money, and results in delays getting drugs to patients. If you over-burden sites and clinical research teams with huge volumes of data this increases the risks of mistakes. I still see many studies where data are collected but are never analysed. It is better to only collect the data you need and dedicate the time saved towards increasing the quality of that smaller dataset.
Did you know that:
In 2016, the FDA published guidance12 for late stage/post approval studies, stating that excessive safety data collection may discourage the conduct of these types of trials by increasing the resources needed to perform them and could be a disincentive to investigator and patient participation in clinical trials.
The guidance also stated that selective safety data collection may facilitate the conduct of larger trials without compromising the integrity and the validity of trial results. It also has the potential to facilitate investigators and patients’ participation in clinical trials and help contain costs by making more-efficient use of clinical trial resources.
Technology, such as Electronic Health Records (HER) and electronic patient reported outcomes (ePRO), drug safety systems and other digital-based emerging technologies are currently being used in many areas of healthcare. Technology such as these can increase data quality but simultaneously increase the number of factors involved. It impacts costs, involves the management of vendors and adds to the compliance burden, especially in the areas of vendor qualification, system validation, and transfer validation.
I may be biased as my job title includes the word ‘Data’ but I firmly believe that data are the most important assets in clinical research, and I have data to prove it!
Scimcon is proud to support clients around the globe with managing data at its highest quality. For more information, contact us.
1White, Christopher H., and Lizzandra Rivrea González. “The Data Quality Equation—A Pragmatic Approach to Data Integrity.” Www.Ivtnetwork.Com, 17 Aug. 2015, www.ivtnetwork.com/article/data-quality-equation%E2%80%94-pragmatic-approach-data-integrity#:~:text=Data%20quality%20may%20be%20explained. Accessed 25 Sept. 2020.
2Alsumidaie, Moe, and Artem Andrianov. “How Do We Define Clinical Trial Data Quality If No Guidelines Exist?” Applied Clinical Trials Online, 19 May 2015, www.appliedclinicaltrialsonline.com/view/how-do-we-define-clinical-trial-data-quality-if-no-guidelines-exist. Accessed 26 Sept. 2020.
3Rothenstein, Jeffrey & Tomlinson, George & Tannock, Ian & Detsky, Allan. (2011). Company Stock Prices Before and After Public Announcements Related to Oncology Drugs. Journal of the National Cancer Institute. 103. 1507-12. 10.1093/jnci/djr338.
4Moore, T. J., Heyward, J., Anderson, G., & Alexander, G. C. (2020). Variation in the estimated costs of pivotal clinical benefit trials supporting the US approval of new therapeutic agents, 2015-2017: a cross-sectional study. BMJ open, 10(6), e038863. https://doi.org/10.1136/bmjopen-2020-038863
5O’Leary E, Seow H, Julian J, Levine M, Pond GR. Data collection in cancer clinical trials: Too much of a good thing? Clin Trials. 2013 Aug;10(4):624-32. doi: 10.1177/1740774513491337. PMID: 23785066.
6Khunti K, Alsifri S, Aronson R, et al. Rates and predictors of hypoglycaemia in 27 585 people from 24 countries with insulin-treated type 1 and type 2 diabetes: the global HAT study. Diabetes Obes Metab. 2016;18(9):907-915. doi:10.1111/dom.12689
7Evans M, Moes RGJ, Pedersen KS, Gundgaard J, Pieber TR. Cost-Effectiveness of Insulin Degludec Versus Insulin Glargine U300 in the Netherlands: Evidence From a Randomised Controlled Trial. Adv Ther. 2020;37(5):2413-2426. doi:10.1007/s12325-020-01332-y
8Ema.europa.eu. 2016. Guideline for good clinical practice E6(R2). [online] Available at: https://www.ema.europa.eu/en/documents/scientific-guideline/ich-e-6-r2-guideline-good-clinical-practice-step-5_en.pdf [Accessed 10 May 2021].
9Pharmacoepi.org. 2020. Guidelines For Good Pharmacoepidemiology Practices (GPP) – International Society For Pharmacoepidemiology. [online] Available at: https://www.pharmacoepi.org/resources/policies/guidelines-08027/ [Accessed 31 October 2020].
10Medical Device Innovation Consortium. Medical Device Innovation Consortium Project Report: Excessive Data Collection in Medical Device Clinical Trials. 19 Aug. 2016. https://mdic.org/wp-content/uploads/2016/06/MDIC-Excessive-Data-Collection-in-Clinical-Trials-report.pdf
11O’Leary E, Seow H, Julian J, Levine M, Pond GR. Data collection in cancer clinical trials: Too much of a good thing? Clin Trials. 2013 Aug;10(4):624-32. doi: 10.1177/1740774513491337. PMID: 23785066.
12FDA. Determining the Extent of Safety Data Collection Needed in Late-Stage Premarket and Postapproval Clinical Investigations Guidance for Industry. Feb. 2016.Industry Leader interviews – Marilyn Matz (Paradigm4)?
Turing award laureate Mike Stonebraker and I co-founded Paradigm4 in 2010 to bring technology from Mike’s MIT lab to the commercial science community to transform the way researchers interrogate and analyse large-scale multidimensional scientific data. The aim was to create a software platform that allowed scientists to focus on their science without getting bogged down in data management and computer science details – subsequently enabling more efficient hypothesis generation and validation, delivering insights to advance drug discovery and precision medicine.
Throughout his 40 years working with database management systems, Mike heard from scientists across disciplines from astrophysics, climatology and computational biology that traditional approaches for storing, analysing and computing on heterogeneous and highly dimensional data using tables, files and data lakes were inefficient and limiting. Valuable scientific data—along with its metadata—must be curated, versioned, interpretable and accessible so that researchers can do collaborative and reproducible research.
We created a technology (REVEAL™) that is purpose-built to handle large-scale heterogeneous scientific data. Storage is organised around arrays and vectors to enable sophisticated data modelling as well as advanced computation and machine-learning. This enables scientists to ask and answer more questions, and get more meaningful answers, more quickly.
Translational research is the process of applying ideas, insights and discoveries generated through basic scientific inquiry to the treatment or prevention of human disease. The philosophy of “bench to bedside” underpins the concept of translational medicine, from basic research to patient care.
There are a number of benefits to streamlining translational research, as it gives scientists the ability to integrate ‘OMICS data, clinical, EMR, biomedical imaging, wearables and environmental data to build a rich, systems-level understanding of human biology, disease and health.
We are actively working with leading biopharma companies globally, as well as research institutes. One of our current projects is working with Alnylam Pharmaceuticals to expedite their research leveraging one of the biggest genetic projects ever undertaken – the UK Biobank. Over 500,000 people have donated their genotypes, phenotypes and medical records. With so much data available on such a large scale, Alnylam’s scientists faced a challenge when it came to extracting meaningful information and making valuable connections that could unlock breakthroughs in scientific research.
The UK Biobank captures genomics, longitudinal medical information and images, so having all that data in one place allows researchers to correlate someone’s traits and presence/absence of a disease, or even susceptibility to diseases like COVID-19, with their genetic make-up. Alnylam has used our technology to help use these correlations to investigate causes of disease and identify potential treatments.
The idea of precision medicine – delivering the right drug treatment to the right patient at the right time and at the right dose – underpins current thinking in healthcare practice, and in pharma R&D. However, until single-cell ‘OMICS came along, researchers were looking at an aggregated picture – the ‘OMICs of a tissue system, rather than that of a single cell type. Now, single-cell analysis has become a major focus of interest and is widely seen as the ‘game changer’ – with the potential to take precision medicine to the next level by adding ‘right cell’ into the mix.
We offer biopharmaceutical developers the ability to break through the data wrangling, distributed computing and machine-learning challenges associated with the analysis of large-scale, single-cell datasets. Users can then build a multidimensional understanding of disease biology, scale to handle more samples from patients with more cells, more features, broader coverage and readily assess key biological hypotheses for target evaluation, disease progression and precision medicine.
By using our platform data are natively organised into arrays that can easily be queried with scientific languages, such as R and Python. The old way of working –– opening many files and transforming into matrices and data frames for use with scientific computing software –– is no longer necessary, because the data are natively “science-ready”. For companies that have tens of thousands of data sets, aggregation of that data in a usable format is tremendously empowering.
Our “Burst Mode” automated elastic computing capability makes it possible for individual scientists to run their own algorithms at any scale without requiring the help of IT or a computer scientist. The software automatically fires up and shuts down hundreds of transient compute workers to execute their task. Any researcher can access the power of hundreds of computers from a laptop.
When Covid-19 hit earlier last year we partnered with a leading pharma company to identify tissues expressing the key SARS-CoV-2 entry associated genes.. We found they were expressed in multiple tissue types, thus explaining the multi-organ involvement in infected patients observed worldwide during the ongoing pandemic.
The first data sets were from the Human Cell Atlas (HCA) and the COVID-19 Cell Atlas. Questions such as “Where is the receptor for SARS-CoV-2” or “What are the tissue distribution and cell types that contain COVID-19 receptors?” can be answered in 30 seconds or less, with responses from 30 or more data sets (since expanded to ~100). More advanced questions can now be investigated, such as the causes for complications and sequelae seen in some patients. Rather than organising all of those data, researchers can focus their attention on unlocking answers.
It has allowed us to support scientists in breaking through the complexities of working with massive single cell, multi-patient datasets. Accelerating drug and biomarker discovery is a key driver for our customers.
The life science community, as well as more commercially oriented research and development groups in pharma and biotech, understand that they need to use leading edge algorithms and cost-effective, scalable computational platforms to give them the ability ask and answer questions in seconds instead of weeks to push forward discovery. Paradigm4 gives the confidence to make earlier and adaptive change decisions that will shorten development, and provide earlier access to complex, real-time data that can detect efficacy and safety signals sooner. Importantly, working in partnership with these users, we will further improve and develop the capabilities in analysing datasets, benefitting researchers as they continue to strive for better results.