Techbio is the interface of biology, technology and engineering. It is evolving as a sub-sector within life sciences as biotechnology and medtech businesses more significantly and systematically utilise data-driven techniques and tools for a variety of applications. We see techbio used:
- To make process efficiencies and improvements (in the bio-engineering space, for example)
- To accelerate the design of, or enable causal analysis of, wet lab experiments
- To accelerate drug discovery and development, and innovation in patient care more generally, and personalised healthcare, in particular
However, given the more material focus on data and data-driven technologies, techbio business models differ from those of traditional life sciences organisations, meaning that the issues and challenges that arise also differ.
Intellectual property (IP) strategies
Techbio is a "blue ocean" market segment, weaving computational analytics and computing with biological understanding. This leads to a new type of IP that is both biological and algorithmic. Developing an IP strategy for this mix requires both specialised advisers (who understand both algorithms and biology) and a customised strategy for protecting arising IP. For example, the computational capability may develop early on into a platform, which may be patentable or held as "secret sauce" trade secrets. Whereas novel biological insights may take longer to surface, these are more likely to merit patent protection.
My recommendation: you should consider holding anything software-related as a trade secret, unless you need to show explicit asset value in licensing or fundraising activities. By patenting computational methods, you may be educating the market, in a space where alternative non-infringing solutions can be devised to compete with your IP. At incubate.bio, we are following this process with our computational platform (ALaSCA).
The “IP mix” within a techbio business differs. There is naturally more dependence on soft IP rights, whether related to algorithms, software and data. These items are dynamic by their very nature in that they will rapidly evolve - this leads to challenges around ensuring rights are effectively “captured” (more on this below). There is inevitably a greater mix of technological and biological outputs to consider when formulating a coherent IP strategy, and different approaches are likely to required for the different output-types.
Having a clear and fit-for-purpose IP and licensing strategy is essential to underpinning value. Patentable inventions will often arise but having a clear understanding up-front of the types of innovations that need to be patented (such as biological outputs) as distinct from what is best maintained as a trade secret, and kept confidential, is critically important. And do note that applicable laws require a trade secret owner to apply appropriate measures to maintain protection over trade secrets, such as consistently applied policies, and NDAs with staff, consultants and commercial partners. IP strategies must also be readily understandable and communicable to staff members as well as to potential investors, licence partners and other stakeholders.
Other IP risks
The dynamic nature of IP generation within a techbio business will mean that the IP-set is developing constantly, and at pace. This leads to risks around loss of IP as it may not be appropriately “captured” at the time. Give early thought to fit-for-purpose IP capture processes. If data/software development work is being conducted by third parties, including third party consultants, agreements will need to be in place to effectively assign ownership of resulting IP to the business as it is created.
There is perhaps an even greater need within a techbio business for data scientists and biologists to work “hand in hand” to identify and value IP effectively. This may require some cultural shift in working practices within an organisation, and a need to ensure that HR policies align with, and facilitate, that way of working.
At incubate.bio, we have spent significant time developing simple IP policies and implementing standard operating procedures (SOPs) behind those policies, which cover the activities of both the biologists and the computational experts. Importantly, these SOPs cover activities where the biologists and computational experts are working “2-in-a-box”. Additionally, we have implemented pragmatic processes to tag, summarise, and value IP across our business.
My recommendation: your IP policies are more than risk mitigation, they fundamentally allow you to capture the value your business is creating. Failure to capture the value can be an unforced error.
IT systems capacity
Having robust and fit-for-purpose IT systems, which are both scalable and support the business into the future, is of critical importance to a techbio business. Do your due diligence on potential suppliers!
Agreements with providers will need to be both robust in protecting and underpinning business need and requirements into the future, and within budget. If systems are being developed for use by the business then are all relevant rights to bespoke technologies assured to the business? Furthermore, how is the business protected in the event of a key supplier’s insolvency?
IT storage, compute and application vendors are many. Scalability is easy to achieve, unless you are storing high resolution images or raw whole genome sequencing data. For the rest of us, public cloud vendors are good enough. The issue is around "fitness", which is driven by IT security as the main concern. We have settled on using an industry-standard password manager, with all IT access linked to the employee’s company email address. The major password manager services offer application plug-ins, which allows choice of IT systems.
My recommendation: don't save costs here, pick the right IT systems that meet your workflow and know-how creation needs. And enforce IT security as an edict, lest incidental issues occur at a later time.
Data access and use
The requirements for storing and processing regulated data and unregulated data are fast converging, especially for patients and consumers. Privacy issues (such as HIPAA in the USA, and GDPR in Europe) form one standard to meet. The other standard is that in the contract when accessing the data. These restrictions are not always aligned, as one is based on government policy and the other is based on organisational policy. Attention to detail is critical.
My recommendation: lead with value creation from the use of the data. This positioning often makes data restrictions easier to navigate.
This is a highly-regulated area, and you will need to address all privacy and other data-related regulation in data use.
Often as important is ensuring that third party data sets used by the business are effectively in-licensed. Data sets are commonly restricted by licence use – the in-licence may allow use for research purposes only. Whether this suffices will depend on how the business plans to utilise the data in question. This kind of restriction may present challenges if biological products are to go into clinical development later on, and then be commercialised.
Understanding how the in-licence regulates derived data will be important too – the licensor, even where the licence is open-source, may confer rights on the licensor, or place ongoing obligations on the licensee (data user).
As Raminderpal says, attention to detail here is critical. Given that many different datasets may be at play, ongoing management of the position is needed. You will be expected to give contractual commitments to licence partners over freedom to use outputs.
Understanding potential regulatory pathways for biological outputs that may arise is important, so that appropriate measures may then be built into standard operating procedures to mitigate against some of the issues and challenges that may later arise.
The regulators (particularly the FDA) are adapting to recognise data arising from advanced technologies (such as machine learning and artificial intelligence (AI)). Keeping an eye on how these regulatory frameworks develop over time is simply a necessity – build into product development plans the ability to flex to meet changes in law and practice. Regulation of AI outputs will address some transparency and trust concerns and may help to build public trust in data outputs arising from advanced technologies.
As James says, this is a highly regulated area, and one of the biggest issues is the natural conflict that comes from the techbio having too much insight into the data. As an analogy, large biopharma often locks away data from clinical trials once drug approval has been obtained, so they cannot inadvertently discover a new side-effect (for example). The balance is one of business interest versus user trust. And techbios have to be especially careful as it is cost-efficient to use computers (versus wet lab experiments) to discover new insights.
My recommendation: as an entrepreneur, trust in data is the #1 attribute to win. It's a long term process, and enables new relationships and collaborations to flourish.
Product liability – managing the supply chain
The supply chain for a techbio business will differ materially from that of a more traditional life sciences business. Downstream partners will expect some contractual assurances, in the form of warranties and/or indemnities, around the insights and other outputs provided under the licence agreement. The form of contract wording to suit will depend ultimately on how the outputs are to be used by the partner, and the precise wording will be key to managing risk for the business, as the licensor.
Where outputs are dependent on outputs provided upstream from another partner then is back to back contract protection in place?
For a techbio, there may be two products – computational (or engineering) platform and biological insights. The latter is the same as in the traditional biotech segment, so the focus in this topic is the former. A techbio platform generates biological insights, therefore the liability is around the insights. There are two ways to manage this liability (other than good contract language), by (i) limiting the scope of any licence (for example, permitting use of insights purely to aid approved clinical decision-making processes such that they can be used to help healthcare professionals with potentially useful information), and/or (ii) having biologically-skilled or even clinically-certified experts in your team interpret and confirm the results.
My recommendation: build a biology and/or clinical team to understand and interpret your computational results. This is how my team is operating.
With the funding environment as it is, many life sciences businesses are looking to use their IP to generate cash through licensing to reinvest in the business. However, any licence deal should align with overarching business objectives. Having a licensing strategy which is clear on what may be licensed and for what purposes, and which is aligned with broader IP strategies, will help to ensure that alignment, and help avoid entering into “early” agreements that hamper plans further down the line, and limit value creation. Given the “IP mix”, a techbio business is going to be more dependent on ensuring that restrictions on licensees stack up.
This is a very important topic, especially in an environment where fundraising is difficult. Services versus licensing income is an important strategy for a techbio CEO to develop. There will be many contracting opportunities, but one needs to keep an acute eye on downstream traps. For example, supporting biopharma customers in disease areas close to your own internal programme may lead to IP being "given away" in services deals which may be important to strategic licensing deals later on.
My recommendation: one needs to be ready to roll out a relevant services offering earlier rather than later, and not wait for cashflow challenges to determine the timeline. But have an upfront plan for keeping angles of separation between the scopes of these activities.
The techbio sector has enormous potential for making life sciences systems more efficient and effective, particularly as the potential for relevant technologies becomes better understood and accepted, and public trust in data insights for life sciences innovation is enhanced. However, techbio is very much an evolution, rather than a revolution, of the life sciences industry.
More about Raminderpal:
Dr Raminderpal Singh is co-Founder and CEO of incubate.bio. His team has built a computational platform to help life sciences and medtech companies rapidly discover and explore causality in biological systems. Dr Singh was previously VP of Sales at Eagle Genomics, where he built the Microbiome business. Prior to that, he was a Business Development Executive at IBM Research in New York, where he took IBM Watson Genomics to market.