In response to our white paper on De-risking an asset we received a comment from Hugo Gagnon, Ph.D., Chief Scientific Officer for Allumiqs:
In today’s competitive drug development landscape, having the right tools to de-risk your asset is key. It’s well known that in addition to using traditional pre-clinical tools, meta analysis suggests that data-driven drug development programs have 2x more chances of success in Phase 2 clinical trials (1).
Using Big Data to educate real-life science decisions starts with a solid data-generation strategy, using a multi-parametric Omics approach on well-designed, robust disease models.
Broadly speaking, the scientific fields associated with measuring cell- or tissue-level biological molecules in a high-throughput manner are called “omics.” (2)
In practice, proteomics seeks to localize, define, and quantify the proteins extracted from biological tissues. A proteomics profile in a pathological animal model lists the changes in protein expression associated with the modelled disease, and identifies targets to recapitulate function in “loss-of-function” diseases, or otherwise re-establish homeostasis via pharmaceutical intervention.
Metabolomics studies all primary metabolites (sugars, amino acids, fatty acids (see lipidomics) and secondary metabolites present in a cell, an organ, an organism. Since metabolic imbalances often result from pathology, metabolomics can be a powerful means of quantifying the efficacy of a treatment.
Lipidomics defines the cellular lipid pathways in biological systems. “Lipidoma” describes a complete lipid profile, and is a subset of the “metabolome” which also includes proteins/amino acids, sugars and nucleic acids.
Supporting a development program thus starts with identifying therapeutic targets using Big Data. It requires creating a clinically relevant animal model, harvesting the tissues and analyzing the “Omics” dataset to highlight which proteins or small molecules have changed -and how- as a result of the disease.
“Pretty much like genomics – the global analysis of genes – we now have access to instruments and knowledge to use proteomics – analyzing proteins and metabolomics – small molecules analysis, to educate efficacy decisions very early in Drug Discovery,” said Gagnon.
1- Gayvert K.M., Madhukar N.S., Elemento O. A Data-Driven Approach to Predicting Successes and Failures of Clinical Trials. In Cell Chemical Biology: Volume 23, Issue 10, 2016, Pages 1294-1301.
2- Committee on the Review of Omics-Based Tests for Predicting Patient Outcomes in Clinical Trials; Board on Health Care Services; Board on Health Sciences Policy; Institute of Medicine. Evolution of Translational Omics: Lessons Learned and the Path Forward. Micheel C.M., Nass S.J., Omenn G.S., editors. Washington (DC): National Academies Press (US); 2012.
In today’s competitive drug development landscape, having the right tools to de-risk your asset is key. It’s well known that in addition to using traditional pre-clinical tools, meta analysis suggests that data-driven drug development programs have 2x more chances of success in Phase 2 clinical trials