Events

Elrig Drug Discovery Digital w/ Amparo Toboso-Navasa

Endotype-driven target identification detection using omics data

Sam Abujudeh, Marika Catapano, Paidi Creed, Jaime Domingues, Craig Glastonbury, Josep Montserrat,  Francesca Mulas, Povilas Norvaisas, Delphine Rolando, Aaron Sim, Amparo Toboso-Navasa, Hamish Tomlinson, Arpad Vezer.


Cancer, sarcopenia, diabetes, and ALS, are but a few diseases that present notable heterogeneity between patients in both symptoms and aetiology. This heterogeneity extends to the patients’ response to experimental treatments, therefore presenting a significant challenge for drug discovery.

Embracing this challenge, at BenevolentAI we have developed an approach using omics data to identify patient subgroups. Here we present this approach, illustrated with a case study from our patient stratification drug discovery programs.

Unsupervised machine learning methods can be used to identify subgroup-specific patterns; the biological interpretation of these patterns is key for the identification of endotype-specific disease-modifying targets. We have developed a systematic evaluation of the identified patterns by assessing confounding variables, clinical covariates and biological mechanisms.  Therefore, biologically meaningful subgroup-specific patterns are defined by the lack of confounding effects, their correlation with clinical variables and their implication in biological pathways. 

Our pipeline tackles the challenges of interpreting subgroup-specific patterns derived from high-dimensional omics data, while uncovering pathobiological aspects of the disease specific to a group of patients. The identification of biological mechanisms that explain a given disease in different patient subgroups will facilitate drug discovery by providing targets regulating those mechanisms.


Amparo Toboso-Navasa

Drug Discovery Scientist

At BenevolentAI, Drug Discovery Scientists with Biology, Pharmacology and Chemistry backgrounds, work together with Bioinformaticians and AI Scientists.  With our joint effort,  we focus on identifying heterogeneous diseases that will benefit from our Precision Medicine approach. We look for omics datasets that reflect this heterogeneity while capturing clinical information; this will allow our models to find patient subgroups. Then, Drug Discovery Scientists, as myself, interpret the underlying biology to find disease drivers in each endotype. Target identification is followed by target validation in experimental settings that mimic the characteristics of each patient subpopulation so we find the right target for the right patient. 



More Posts

You Might Also Like

News
Data from Eli Lilly’s COV-BARRIER trial shows baricitinib reduced deaths in hospitalised COVID-19 patients by 38%
The latest data published in Eli Lilly’s Phase 3 randomised, double-blind, placebo-controlled study (COV-BARRIER) shows the largest clinical effect reported to date for a reduction in mortality in the COVID-19 patient population
Apr 8, 2021
News
BenevolentAI named as one of Fierce Medtech’s Fierce 15 of 2020
BenevolentAI was selected as one of the most promising private companies in the industry by Fierce Medtech in its Fierce 15 2020 list.
Mar 8, 2021
Blog
Tech Nation Visa: the gateway to world-leading UK tech jobs
Drawing attention to the Tech Nation Visa, a great initiative that enables the brightest international talent to live and work in the UK.
Feb 19, 2021
News
BenevolentAI announces first patient dosed in its Atopic Dermatitis clinical trial
A molecule designed and developed by BenevolentAI to treat mild to moderate Atopic Dermatitis has entered clinical trials.
Feb 11, 2021
News
BenevolentAI and AstraZeneca achieve collaboration milestone with novel AI-generated chronic kidney disease target
BenevolentAI and AstraZeneca hit collaboration milestone with an AI-generated CKD target from the partnership entering AstraZeneca’s portfolio.
Jan 27, 2021
News
ACTT-2 trial results published in the New England Journal of Medicine validate baricitinib’s efficacy in combination with remdesivir in hospitalised COVID-19 patients
Peer-reviewed data from the ACTT-2 further validate BenevolentAI’s hypothesis for baricitinib as a potential COVID-19 treatment.
Jan 15, 2021