At the Vanguard of the Life Sciences Digital Transformation Wave

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Summary: Learn how to leverage digital transformation strategies and cloud lab platforms for faster drug discovery using closed-loop, design, make, test & analyze automation systems.


In the late 1800s, the world wanted a faster horse, so Henry Ford built the world a horseless carriage - the car. In our era, drug discovery needs more rapid discoveries and innovation. However, using our existing methodology and infrastructure creates bottlenecks and inefficiencies that curtail and delay the discovery of new life-saving therapies.

Like Henry Ford did for the transportation industry, Strateos has affected a paradigm shift for the life sciences industry. They are initiating a convergence of information technology and science, thereby becoming the leading edge in the life sciences and digital transformation wave. They have created the world’s first Cloud Lab Automation-as-a-Service (CLAaS)

platform to reimagine the lab as a rich data center to progress the life sciences at a breathtaking pace. And they help their clients turn their digital transformation into a business transformation.

Before we share the benefits of partnering with a digital transformation enablement organization like Strateos let’s take a step back in time and look at the origins of drug discovery to grasp the strides we have made thus far.

Let’s begin our journey when technology was not part of the equation; drug discovery goes back thousands of years. Before modern innovations, drug discovery was based on more primitive observations and experimentation with natural products like plants, flowers, and fungi. Some examples include:

In the 1600’s, Swiss alchemist Paracelsus von Hohenheim created laudanum, known as tincture of Opium today, and contained almost all of the opium alkaloids, including morphine and codeine as well as gold and pearls. This pain killer and cough suppressant was probably one of the earliest examples of a “medicine” and germinated the seeds of the pharmaceutical industry.

The next colossal leap took place in the 1700’s, when the earliest inoculums were prepared for smallpox. William Withering used his interest in botany to discover a treatment for dropsy by extracting digitalis from the leaves of the foxglove. James Lind revolutionized the British Royal Navy when he discovered that scurvy was related to a lack of Vitamin C, by performing arguably the first controlled medical trial.

The 1800's brought us further discoveries and medicines such as:

As scientific innovation progressed in the 1900s, drug discovery became more systematic and took an industrialized approach. The process evolved to screening natural products and then isolating the active ingredients.

Synthetic versions of natural product(s) were produced where the chemistry wasn’t too complicated. The disadvantage at that time involved safely testing these ingredients. In fact, many early chemists would test compounds on themselves and then record the outcomes-if they survived! Fortunately safety and toxicity studies have significantly improved since these early days.

With the advent of the mainframe and personal computer, drug discovery has become one of the world’s most complicated and technical businesses. At this juncture of scientific progress, advanced lab automation, digitalization of the laboratory, and the power of an in silico-first approach are all rolled up under the latest Cloud Lab infrastructure. An automated loop of Ideate, Design, Simulate, Make, Test, and Analyze will put human intuition, creativity, and ideation to the test and has the potential to catapult results beyond our expectations.

This concept can drive down cycle times, make science reproducible, and drive FAIR data and process principles. All of which will only enhance the future in silico-first efforts with more model quality data. We are entering a new phase of computational chemistry where scientists are edging ever closer to designing and predicting active molecules.

Though it has taken over 375 years to reach this point of advancement, the exponential speed of innovation could take us even further in the next 375 years. We are now driving much faster cycle times with ideas being quickly validated with automated, diverse, and novel synthesis followed by rapidly generated screening data. Like Cloud computing, your Cloud experimentation and testing are remote, secure, and highly efficient.

Because of the cutting-edge nature of these R&D technologies, many scientists are unacquainted with the benefits they provide. These include high throughput experimentation (HTE), advanced lab automation, and in silico-first or model-first approaches to R&D.

However, precisely because these methods are so new and rapidly-evolving, users can experience some change management issues and consequently encounter failures. As is often true, innovation has outpaced mainstream acceptance. For example, in 1963 DARPA (the Defense Advanced Research Projects Agency) partnered with MIT for what has now become collectively known as cloud computing.

The availability of cloud computing to the public started with Amazon Web Services in 2006, and IBM and Google followed suit a year later in collaboration with several universities. The goal of this collaboration was to provide cloud computing to all universities.