#TechBio - evolution or revolution?
Graham Combe
Founder of the #CreativeDisruption & #AgileLeaders Forums & #BiotechBuddies and #CoffeeBuddies Events
An Agile Leaders CEO Forum White Paper write-up on 3/11/2022
Topic – Harnessing Tech Bio
Written by Graham Combe, BioSell and edited by Lynne Trowbridge, SciTribe.
Topic content driven by Dr. Raminderpal Singh, incubate.bio and Prof Tony Sedgwick
The information in this write-up has been gathered at the Agile Leaders CEO Forum held at the Mills & Reeve London headquarters on King William Street on 13 September 2022.?It’s a summary of the thoughts and comments made by the participants under the Chatham House Rules.
Our next #TechBio #AgileLeaders CEO Forum will be held in Manchester from 1.30 pm at Mills & Reeve’s new Manchester offices at 1 Circle Square, Symphony Park, Manchester M1 7FS.?If you have any comments, questions or ideas that you would like to debate about #TechBio, or simply to learn from others, please go to Eventbrite for more details and tickets.
For those that can’t make the afternoon, but can make the evening we have #BiotechBuddies Manchester from 6.30 pm – 9.30 pm, details on Eventbrite:?
What is the Agile Leaders CEO Forum?
The Agile Leaders CEO Forum is an informal and friendly “roundtable” gathering of life sciences innovation leaders to discuss a popular topic moderated by esteemed industry & academic resident expert Prof Tony Sedgwick.?The topic for this session was “Harnessing Tech Bio” and was co-hosted by Dr Raminderpal Singh in this instance.?The focus of our Agile Leaders CEO Forum series is the “Business of Life Sciences Innovation.” With this event, our focus was on “how tech is disrupting/converging with the life sciences and healthcare sectors.”
Tony is a self-proclaimed “Thought-Disruptor” and author of the bestselling science management book “Mighty Advisor.” The event is organized by Graham Combe Founder of BioSell, a leading life science innovation marketing and events consultancy.?This Tech Bio event was co-hosted with Raminderpal Singh, CEO, Incubate.Bio who is a data scientist expert with years of experience in the life science innovation sector. Incubate.Bio an emerging Tech Bio start-up implementing causal analysis and ML.
Agile Leaders CEO Forum from 13 Sept 2022 Write-up starts here:
The topic for discussion was “Harnessing Tech Bio” with a particular focus on the “Business of Life Sciences Innovation” and how ‘tech’ is disrupting/converging with the life sciences and healthcare sectors.
What is our definition of Tech Bio?
Tech Bio is a term used generally to describe how computational technologies – including artificial intelligence (AI), machine learning (ML), and causal analysis – are used to achieve better outcomes across the life sciences innovation sector.?This includes in drug discovery, pre-clinical development, and clinical development across the full breadth of the healthcare innovation sector.?
Tech Bio is a relatively new term that is being rapidly adopted and widely used, however many are sceptical that it is a new sector in its own right.?It's opening up new capital, however, as the life science innovation sector converges with the tech sector.?Tech Bio involves transformative tech and is impacting the healthcare sector more and more, as data management tools facilitate access to data and computing power gets greater.?Brisk discussions resulted in a consensus that TechBio, using AI/ML and causal analysis, is an evolution rather than a revolution.?
Many of the techniques being harnessed by technology have been used before by scientists.?Tech is enabling scientists to widen the scope of their hypothesis and have higher data throughput through increasing computer power.??The scientific hypotheses are the same, but more speed increases the likeliness of more discovery or simply faster discovery.
The life sciences industry has been slow to embrace Tech to its full potential, partly due to the scientific complexity involved, but also the constraints of strict regulatory frameworks within which the sector operates. It was harder for the industry to push new boundaries with computational techniques, they felt and believed they were constrained, … or were they just falling behind and delaying the inevitable?
The COVID pandemic has been a major disruptor to the status quo and forced the healthcare sector to radically evolve its ways of working as the entire sector focused its efforts to find a solution to the virus, quickly.?Expert groups collaborated as never before, and active use of “Real World Evidence (RWE),” a relatively new concept enabled by aspects of the Tech evolution, became a reality overnight, literally.?What’s more, more money flowed into the sector, not just from traditional healthcare investors, but from new investors in tech targeting disruptive innovations.
To set the scene and provide a common frame of reference, Dr Raminderpal Singh, CEO, Incubate.Bio talked the participants through his pragmatic six-step guide to implementing a world-class data and data analytics strategy, as follows:
1.??????Build computational capabilities incrementally, achieving measurable wins at each phase.
2.??????Treat data as an asset on the balance sheet, not just an input to or output from your analytics.
3.??????Use vendor software for data management and databases, e.g. for knowledge graphs.
4.??????Only implement the analytics you need, based on the known business or scientific questions.?Build the expertise to identify, prototype and qualify those analytics.
5.??????Analyse the reasoning and mechanisms explaining your computational results, i.e. go beyond the correlations to understand causality.
6.????? Invest in your science team to be statistically strong.?Build team skills (not just code) in Exploratory Data Analysis (EDA) including rapid model prototyping.?
Raminderpal also candidly explained the key challenges that he had experienced firsthand while supporting other life science organisations. He listed the key challenges as follows:
a.??????How to ensure that basic blocking & tackling over data analytics is done as needed, cost-efficiently and in a scalable extensible way?
b.??????How to set up the contracts with partners, customers, and vendors so that you have the necessary tactical and long-term data freedom of use??
c.???????How to analyse the data accessible to you to enable new business questions to be described and then answered - supporting faster customer acquisition, new customer type acquisition and business model testing??
d.??????How to design and implement a schema/ERM (Entity Relationship Map) to ensure the exploitation of your datasets in a scalable way?
e.??????How to quantify the value of the datasets available, such that investment/ROI decisions can be made - including i) analytics development time and ii) data acquisition & generation costs??
f.???????How to model the strength and type of intervention needed to obtain the highest efficacy and the lowest risk of adverse effects?
g.??????How to discover which factors are having an undesired and unexpected effect on the product/drug’s performance?
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Discussions then moved to cover tactical issues and considerations around data, how to use it efficiently and effectively, and understanding its true value. Life Science Innovation companies often have loads of data and analytics to integrate that data.?However, many don’t fully comprehend the potential value of this asset. High-quality curated and contextualised data is critical.
Data Integrity is paramount.?How can you ensure the integrity of your data??Can you only rely on data that you have produced yourself??How reliable are external sources??In the future will investors insist on seeing raw data to evaluate a company??Do you spend money to ensure that you keep all your raw data??Or is it just the raw data that you crunch that you keep?
These were just some of the questions that arose during the discussion.
The group was asked “who employs a dedicated data scientist?” to which only one company of the 14 attending did.?Data scientists need to understand the data and then be able to analyse it.?Data analysis tools can be sourced commercially, it is not necessary to create everything in-house. It was agreed that what is important is that your scientists understand statistics, and whoever is analysing the data needs to understand the science to know what they are looking for.?Typically, there is a divide between “wet lab work with analytical support” and then “data & analytics.”?The scientist is the “super-human” here and needs to be enabled to work across both areas.
When balancing hype and caution, caution is your friend. In-silico (computational) innovation allows you to look at different spaces that you wouldn’t look at normally and it allows you to look at larger data sets.?However, you need to be clear on what you want to achieve before seeking external advisers. Computation can accelerate your progress.?As the business leader, “do you fully understand the areas of your business that can be digitally transformed?”?“Is there more you can do with tech?” these are the questions you need to ask to fully understand where the value is.?
The data management has progressed significantly and is now a standardized commodity that can be easily and successfully outsourced. The true value-add comes from interrogating the data with the right tools to get the answers you need.?Knowledge and understanding of statistics remains a vital skill and many groups are now building Exploratory Data Analysis (EDA) capability, including rapid model prototyping to facilitate effective working.
Then there is Causal Analysis that uses Bayesian hypothesis to unlock unknown relationships and interdependencies – this is important when presenting to groups like regulators.?They aren’t just going to accept this is a “black box” solution, they need to understand the “causation,” like the mechanism of action.?Causal analysis helps you prove your data wasn’t just a fluke!?This is where our co-host Raminderpal has key expertise, and he has consulted with many biotech companies engaging in AI/ML.
The participants were asked, “How do we improve the adoption of tech in life sciences innovation organisations?”
Everybody needs to speak a common language.?
Apparently, when Google’s Deepmind decided to embark on their AlphaFold project to predict protein folding, they got their 10 best tech people and then brought in leading scientists to teach them the relevant biology.?They were taught everything they needed to know about protein folding first.?Once they gained this biological knowledge, only then did they start programming their solution.?
We are hearing that investors are saying AI/ML are producing very little so far, so the jury is still out on how effective tech will be in improving the drug discovery process.?Despite considerable investment to date in AI/ML to enhance drug discovery and development, there are only a couple of examples of FDA approval success and one of these is repurposed drug in the fight against COVID.
What AI/ML can be used very successfully for is to trawl multiple sources of information and give a “confidence score” for the potential of a particular molecule or target. AI/ML has proven extremely useful in the area of image recognition and is becoming common place in diagnostic health innovation, benefitting both patients and the clinical community.
The following key take home points were noted by the participants of this Agile Leaders CEO Forum:
The following key Recommendations were summarised at this Agile Leaders CEO Forum:
1.??????Treat your data as an asset.
2.??????Don’t separate data science from data & statistics.
3.??????Important to bridge the understanding and communication between different groups of experts – i.e. biologists, chemists, physicists, engineers and data scientists.
4.??????Make sure the data is available for other people to use, opening the chance to trade this data.?
5.??????Scientists have their hypothesis based on the information they gather “where AI/ML is fishing, scientists are more focused.” This has been likened to a spotlight, where the AI/ML looks in the dark areas where the scientist isn’t looking.???
6.??????What you put in will determine what comes out.?The importance of cleaning your data and ensuring high-quality external data is very high.
7.??????Causal analysis enables a new approach to significant value in the drug discovery and development (pre-clinical) workflow.?It extends the insights offered by AI/ML to something actionable.
Raminderpal offered to help life science companies with the above Recommendations. He can be reached at [email protected]
Our next #AgileLeaders CEO Forum to debate these points, ask more questions and to inform others is 22 November 2022 in Central Manchester at Mills & Reeve, 1 Circle Square, Symphony Park.?Raminderpal and Tony will there along with other seasoned professionals.?For tickets & further details go to Eventbrite:?https://www.eventbrite.co.uk/e/420467388277
For those that can’t make the afternoon, but can make the evening we have #BiotechBuddies Manchester from 6.30 pm – 9.30 pm, details on Eventbrite:?
Thanks to:
Prof Tony Sedgwick for moderating an enthralling debate on Tech Bio, and being our resident “thought-leader for all our Agile Leaders CEO Forums to date, and #coffeebuddies during the pandemic.
A special thanks to Raminderpal Singh of Incubate.Bio for sharing his extensive TechBio knowledge
A huge appreciation goes to the Mills & Reeve staff who not only participated in the debate and provided the venue, but also provided support of our evening #biotechbuddies
Mills and Reeve have one of the largest full-service life sciences teams in the UK. Renowned lawyers with extensive expertise in the industry that will provide the solutions to enable you to seize the opportunities open to you.
Thanks to Dianne Lee of DLRC for providing sponsorship support and participating in the discussion. DLRC is a dedicated team of highly qualified and experienced Regulatory Affairs professionals.
We look forward to next hosting the Agile Leaders CEO Forum in Manchester at the Mills & Reeve offices there on the afternoon of the 22 November, followed by #BiotechBuddies Manchester.
Thank you to Lynne Trowbridge of SsciTribe who participated in this discussion and edited this feature.
C-Level Executive | Founder | Advisor
2 年Graham, good to see this event is touring the UK. Giving everyone the opportunity to attend in person.
Looking forward to being there Graham Combe Tony Sedgwick
Communications Consultant with a science PhD
2 年Great to see you guys expanding this initiative beyond the golden triangle!!
Director, Global Business Development at Endpoint Clinical
2 年Looks like it’s going to be a great event! Extremely relevant topic as well!