Accelerating biotech innovations and predicting genetic codes: Spotlight on Ingenza

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Meet Ian Fotheringham, the Managing Director of Ingenza, a global CRDMO that aims to accelerate clients' journeys from discovery to clinical trials while minimising costs and risks. The company's innovative codABLE® algorithm brings predictability to DNA sequence configuration, revolutionizing recombinant production of pharmaceutical and industrial molecules. Join us as we explore Ian's insights and Ingenza's ambitious goals for the future.

Tell us about your company and ambitions

I am the Managing Director and founder of Ingenza, a global CRDMO specialising in the design and development of biological manufacturing systems for large and small molecule pharmaceuticals, as well as sustainably prepared industrial products. Our team of highly skilled experts in microbial strain engineering has access to 12 different host organisms, offering our customers a wide range of options to advance their products of interest, and the flexibility to seamlessly transition to a different host if the initial approach hits a roadblock. Essentially, we can cater to the diverse production needs of the biotech industry.

What is your goal?

Our goal is to accelerate our customers’ journeys from discovery to the onset of clinical trials while minimising costs, risks and time to completion. Ingenza currently specialises in product development to first-in-human clinical trials, at which point we support technology transfer back to the customer, or to a CDMO partner to conduct subsequent clinical stages. Our comprehensive manufacturing package includes all critical elements of upstream and downstream production, quality management and comprehensive product characterisation, making it easy for our customers to continue their product development with minimal disruption.

What is Ingenza’s biggest achievement so far?

Our greatest achievement is bringing greater predictability to the recombinant production of pharmaceutical and industrial molecules. For example, configuring DNA sequences to produce a target protein efficiently is a persistently unreliable process, relying on empirical, often time-consuming, iterations to assess sequence changes. Accelerating this process has been a major achievement for Ingenza, which we realised through our codABLE® machine learning algorithm. Once the software has been trained using a vast dataset of genetic sequences, it can accurately predict an optimal DNA sequence for a given protein. Our approach has proven successful in bacteria – such as Bacillus – and is now being expanded to the industrial yeast Pichia pastoris, and mammalian hosts, such as Chinese hamster ovary (CHO) cells.

What are some of the priorities that you’re currently working on?

We are currently enacting a cohesive and focused growth strategy for the business, based on strong communication with key stakeholders and a clear vision for our future. This is allowing us to further refine our customer offering, expanding our market reach and completing projects faster. Data-driven methodologies are also enabling us to use customer feedback to evaluate our performance and find ways to continuously improve our services.

What excites you about the UK life sciences sector?

We are excited by opportunities to partner with nascent biotech companies, helping them overcome demanding hurdles and gain international traction in bringing life-changing products to UK and global markets. For example, Ingenza’s biomanufacturing support was crucial to propel one UK partner's first-in-class antimicrobial peptide to a Department of Health contract and acquire international investment. A second Ingenza bioprocess is advancing another UK partner’s low-cost haemophilia treatment to address a vastly underserved global market. Making a difference in these ways is what we are all about.

If you could invite any scientist or entrepreneur to dinner, who would it be and why?

Alan Turing, the man behind the world’s first computer. I would be curious to get his view on AI and how he imagines it will evolve in the future, as well as what applications he sees for it that no one else has thought of and what the limitations will be. It would also be interesting to have his opinion on our codABLE® gene design algorithm!

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