RESPOND3 - Responsible early Digital Drug Discovery
We aim to find new antibiotics and therapies for Chronic Obstructive Pulmonary Disease through the development of improved high-throughput computational methods. The project is built on a foundation of responsible research and innovation principles to align research objectives, processes, and outcomes with societal expectations.
In order to maintain the high health standards our society is accustomed to, there is an increasing need for innovations in drug discovery. The development of a new drug takes 15-20 years and costs billions of dollars. Without a more efficient path to drug discovery, many promising candidates will be lost in the “valley of death” and fail to be transformed into a drug that can benefit the public. RESPOND3 is developing a new high-throughput computational method to accurately identify candidates early in the discovery process. Through this project, we aim to discover and develop new antibiotics and better therapies to treat Chronic Obstructive Pulmonary Disease. The project design will be informed by the principles of responsible research and innovation through a sustainable and balanced stakeholder engagement framework. This project is funded by the Research Council of Norway and is one of the multidisciplinary research projects within Centre for Digital Life Norway.
Drug discovery begins with basic research to identify a critical part of the disease, a gene or protein, and follows with a search for molecules that can bind with that part and disrupt the disease. Testing and optimizing thousands of potential molecules requires a lot of resources and labor. Computer algorithms can reduce the workload by suggesting which molecules are worth testing and which can be ignored. However, these methods are computationally demanding and often inaccurate, leading to lost time and money on experiments that don’t lead to a potential therapy.
RESPOND3 is making this process more efficient and accurate with two new algorithms trained on a larger academic library of 20 000-100 000 potential molecules. This project is designed and built on a foundation of responsible research practices which aims to be responsive to and inclusive of relevant stakeholders interests in order to maximize the societal value of publicly funded research.
The field of machine learning has enabled the development of techniques to uncover complicated patterns and connections in large, heterogeneous data sets. We will capitalize on the fact that modern machine learning is starting to show its usefulness in drug discovery, particularly in the optimization of ligand scoring functions and binding affinity prediction.
Improving the characteristics of a candidate once it has been found will optimize its performance. Changing characteristics like solubility or cell membrane permeability allows researchers to design drugs that can effectively reach their target or to control whether a drug needs to be injected or swallowed so that patients can take it easily.
Structural information is essential to understand how compounds interact with targets in order to optimize their performance. X-ray crystallography will be used to determine the three-dimensional structure. In vitro assays will be used to determine activity, stability and toxicity.
One of the core objectives of responsible research and innovation (RRI) is to maximize the societal value of publicly funded research. RRI encourages production of new innovations through societal engagement and collaborative research. RESPOND3 aims to develop a sustainable, effective, and balanced stakeholder engagement framework to inform RRI in drug discovery and development. By building trust with stakeholders at the outset of the innovation process and aligning research objectives, processes, and outcomes with societal needs, RESPOND3 can develop therapies that are not only effective but also meet the particular needs of specific patient populations.
University of Bergen
Western Norway University of Applied Sciences
University of Copenhagen
|Sheikh, Zainab Afshan||Assistant Professor|
Nathalie Reuter, Professor, University og Bergen
Alexander Selvikvåg Lundervold, Associate Professor, Western Norway University of Applied Sciences
Bengt Erik Haug, Professor, University og Bergen
Ruth Brenk, Professor, University og Bergen
Pushpak Mizar, Postdoctoral fellow, University og Bergen
Parveen Gartan, PhD Candidate, University og Bergen
Fahimeh Khorsand, Postdoctoral fellow, University og Bergen
Fábio Oliveira, PhD Candidate, University og Bergen
- Yu, Helen, External Researcher, University of Copenhagen
RESPOND3 - Responsible early Digital Drug Discovery has received a four year funding from The Research Council of Norway
Project: Towards better computational approaches and responsible innovation strategies in early drug discovery - application to antibiotics and COPD
(Grant number: 294594)
Period: 01.01.2019 to 31.08.2023
PI External Researcher