BiopharmaTrend.com

Pharmacogenomics seeks to understand underlying genetic markers that influence treatment responses and modification of therapies to increase efficacy treatment success (Monte et al., 2012). This field has greatly benefited from classical approaches to identify polymorphisms and the molecular basis involved in drug interaction with targets their activity with treatments.

Next generation sequencing makes it possible to simultaneously study DNA polymorphisms across the genome, couple it with RNA analysis and the proteome comparably faster and cheaper. Ideal pharmacogenomic markers should be easily detectable and integratable into improving personalized medicine (Chauvin and Boisvert, 2018). Here we present some of these case studies and how information gleaned from DNA, protein and [RNA analysis] has helped improve our understanding of treatments and tailor medicine for specific case studies (personalized medicine).

Molecular mechanics (MM) is a traditional computational approach when it comes to modeling in synthetic organic chemistry, medicinal chemistry and versatile aspects of drug design. However, MM methods have significant limitations, for example, when used to study electron-based properties within the drug-receptor microenvironment. Quantum mechanical (QM) methods allow to substantially increase the accuracy of predictions and provide much more relevant models of chemical and biological objects and their interactions, but QM methods are extremely (often prohibitively) computationally costly.

However, a series of advancements over recent years allowed to expand horizons in this direction, for example, the emergence of density functional theory (DFT), the overall increase in the computation power and the emergence of distributed cloud-based computational infrastructures.

About one in three human proteins is understudied. Even when quantifying data is available from multiple sources, "dark" genes and proteins are simply not well characterized (Figure 1).

There may be multiple reasons why dark genes are ignored, although the literature isn't always in agreement with data availability.

Are you curious where dark gene hunting leads? There are a number of resources:

In a seminal work published by McKinsey & Company – The Granularity of Growth, a detailed study of the performance of the 100 largest US corporations over the two most recent business cycles – a key finding emerged: top-line growth is vital for survival.

To dig a bit deeper, the study found that a company whose revenue increased slower than the growth of U.S. GDP was five times more likely to falter in the next cycle than a company that expanded more rapidly. Companies have a “Growth Imperative,” which is to either “grow or go,” the authors concluded; in other words, companies must grow faster than their peers or else be left behind.

Back for its second year, the AI-Machine Learning Clinical Development Summit will help you discover how to unlock the power of AI and Machine Learning to revolutionize clinical trial success and accelerate getting better drugs to market.

Join your peers to listen to real world use cases of AI in action from the likes of Cloud Pharmaceuticals, Bristol-Myers Squibb, Abbvie, Celgene, Pfizer, Merck, Leo Innovation Lab, Takeda, Ieos Digital health and more!