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Towards better computational approaches and responsible innovation strategies in early drug discovery

Together with Nathalie Reuter and Bengt-Erik Haug from the Chemistry Department and Alexander Lundervold from the Høgskulen på Vestlandet (HVL) we got funding for the project «Towards better computational approaches and responsible innovation strategies in early drug discovery: application to antibiotics and COPD» under the Centre for Digital Life umbrella. In this project, we are aiming toward more responsible and efficient drug development.

DLN
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DLN

And this is what we are trying to do:

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 an average of 4 billion dollars. While some drugs have been discovered through trial and error, modern drug discovery often starts from a validated biomolecular target for which modulators are sought using systematic strategies (Cf. Fig 1). Computational methods are part and parcel of the modern drug discovery pipeline and their use has the potential to accelerate and render the hit-to-lead step more cost-effective and safer. Yet, there are limitations pertaining in particular to accurate high-throughput prediction of affinity between drug targets and potential drug candidates. This creates a bottleneck where only computationally demanding methods can be applied to reach the accuracy needed for lead identification. Through this application, we aim at advancing two drug discovery projects addressing the need for (1) new antibiotics and (2) better drugs to treat Chronic Obstructive Pulmonary Disease (COPD) through the development of improved high-throughput computational methods. Further, while publicly funded research is an indispensable actor in modern drug discovery, the current technology transfer model renders translation from academia to market challenging and results in the so-called Valley of Death. Stakeholder involvement is necessary for transparency and generating socially robust knowledge but not always easy to implement in such a way that the discovery project can respond to its feedback. Therefore, we also aim at proposing a model for stakeholder involvement and their influence on early drug discovery projects. To reach these ambitious goals, we have designed a truly transdisciplinary project for which we have assembled an international team with expertise in organic synthesis, medicinal and computational chemistry, biochemistry, structural biology, mathematics, computer science, social sciences and law.