Events

TOPRA Fellows' Webinar: Data Diversity and Inclusion

It is well documented that the lack of representation in biomedical research is leading to a data gap that can no longer be overlooked if we are to avoid exacerbating existing health inequalities in the age of digital health and precision medicine.

Advances in machine learning (ML) techniques are allowing the scientific community to unlock the potential of biomedical data and extract valuable insights like never before. Yet amidst the hope sits a certain uncomfortable reality: not everyone is set to benefit from these advances. At the heart of innovation in healthcare lie the datasets used to train the algorithms, such as data from scientific literature, clinical trials, omics, and patient real-world data. These datasets are the lifeblood of new technologies. Yet, they have significant shortcomings, since the majority of medical research is conducted on white and predominantly male populations of European descent. This lack of diversity in data has serious consequences for medical care, as the products discovered through the use of these data may not benefit everyone. For example , as Covid-19 is already disproportionately affecting people of colour, working with data sets that do not include that population equally could further exacerbate the health disparities.

→ Read the full blog here

As the industry now seek to put in place solutions, it is important to highlight the role that regulators play. The FDA is at the forefront of this; the question is what is EMA doing?

Questions:

  1. Is there an issue with diverse representation clinical research and is this recognised by European regulatory agencies?
  2. EMA has a number of guidance documents on considerations for sub-populations such as patients with impaired elimination, the elderly, children, women and ethnic subgroups. Are these guidance documents making sufficient impact on inclusion and diversity in research?
  3. What role do regulators have in ensuring adequate representation in data supporting marketing authorisation

→ Register here


Peju Oshisanya

Peju Oshisanya

Clinical Drug Development Leader at BenevolentAI

Peju is an innovative operational strategy expert with over 15 years wide-ranging experience relating to strategic programme leadership, planning and management of clinical trials with responsibility for global clinical programmes. She has extensive experience in working in early drug discovery and exploratory phases focused on the transition of early stage assets to clinical development. She has held leadership positions in programme management responsible for key clinical programmes and assets within Eli Lilly, Sanofi Aventis, Pfizer and Takeda. In her current role at BenevolentAI, she is responsible for driving the asset strategy to maximise the value of both early and late phase drug development programmes.

More Posts

You Might Also Like

Blog
Intern at BenevolentAI part I: meet our 2020 intern cohort
What impactful work did our interns get up to across Engineering, Data Science, ML and business operations this summer? Get to know them and their work in our tech internships blog.
Nov 26, 2020
News
FDA grants Emergency Use Authorisation for baricitinib in hospitalised COVID-19 patients nine months after initial hypothesis was published by BenevolentAI
BenevolentAI scientists first identified baricitinib as a potential treatment for COVID-19 in early February 2020 using Benevolent's AI tools and biomedical knowledge graph.
Nov 20, 2020
News
BenevolentAI at NeurIPS 2020: Machine Learning in Drug Discovery
BenevolentAI is happy to announce it is sponsoring NeurIPS 2020. Join us to hear about data diversity and ML applied drug discovery, and to learn about careers in the field.
Nov 17, 2020
Blog
Careers with Impact: 5 learnings from machine learning applied drug discovery
Last week, we brought together four of our exceptional colleagues for a panel discussion on careers in machine learning applied drug discovery. Here are some of our main takeaways:
Nov 17, 2020
Blog
Data published in Science Advances shows baricitinib reduces COVID-19 morbidity and mortality
Research published in Science Advances supports BenevolentAI’s AI-generated hypothesis from late January for baricitinib as a treatment for COVID-19.
Nov 13, 2020
News
Sir Nigel Shadbolt joins BenevolentAI as a non-executive director
BenevolentAI strengthens its Board with the appointment of AI pioneer Sir Nigel Shadbolt as Non-Executive Director.
Nov 3, 2020