A ‘truly translational mindset’ requires the correct tool for the correct job: Synlogic

By Maggie Lynch

- Last updated on GMT

(Image: Getty/shironosov)
(Image: Getty/shironosov)

Related tags Translational medicine translational sciences Clinical research Patient centricity Clinical data preclinical preclinical development

Translational research faces new challenges – and opportunities – with the rapid growth in precision medicine and new drug modalities, as the industry looks to quickly bring new treatments to patients.

Aoife Brennan, president and CEO of Synlogic, will be speaking next week on a panel discussing the evolving models for translational research collaboration, new approaches to which could help accelerate the drug development process.

Synlogic uses its proprietary discovery platform to develop synthetic biotic medicines for rare genetic diseases and is currently collaborating with pharmaceutical companies like AbbVie, using its platform to help develop treatments for other diseases.

We spoke with Brennan ahead of the conference to further discuss the opportunities and challenges to translational research – the bridge between basic science and patient care​.

Expanding on this definition, Brennan said, “While it can sometimes be described as a phase of development – i.e. from target identification to proof of concept – in my view, it is a critical mindset even out to post-marketing.”

Brennan further explained that translational science is collaborative by nature​. “With recent advances in new tools and modalities as well as changes to how we do development, it requires even more expert collaboration,” ​she said.

An example of this, Brennan explained that for some new modalities, manufacturing issues can have a significant impact on the translation speed from bench to bedside.

Additionally, patient advocacy organizations are getting involved at earlier stages of development. “So, the number of stakeholders in development is increasing all the time,”​ she added.

But advancements in tools, research, and even discovery platforms, require translation​ into what Brennan refers to as “true health gains.” ​She added that these gains require traversing ‘the valley of death', and to do so, there needs to be further investment in basic science as well as policy and regulations.

Beyond regulations and biologic discoveries, there is also an opportunity for artificial intelligence (AI) to support translational science. AI can be used to create research models​ that can allow further prediction of how a biologic advancement can relate to drug development and how that drug can worth within a patient.

Brennan told us that with AI, “Our exponential increase in computing power has had and will continue to have an impact on every phase of development but a truly translational mindset requires us to select the correct tool for the correct job, to understand the assumptions and to interpret the resulting data appropriately.”

BIO International takes place next week in Philadelphia, PA.

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