We are entering the pharmaceutical golden age

  •   6 min reads
We are entering the pharmaceutical golden age

There are many who argue that innovations in medical science, especially in pharmaceuticals, are slowing. They are wrong. We are approaching the pharmaceutical golden age.

On January 10th 2020, Chinese scientists revealed details of the sequencing of the COVID-19 genome to the world. Within three days of the Chinese research being made available, Gregory Glenn, who is responsible for R&D at Novavax, ordered the gene for the viruses spike protein. Within two days of receiving the Covid genome sequence, Moderna had designed its mRNA based vaccine.

Technology cynics said it would take years to develop a Covid vaccine. They were wrong because they didn’t understand something fundamental about innovation.

In an often-cited report, published in 2020, entitled Are Ideas Getting Harder to Find?, the authors argue that the amount of required research per innovation is increasing. The report takes a broad look at innovation, but in the section looking at mortality and life expectancy, the authors, Nicholas Bloom, Charles I. Jones, John Van Reenen, and Michael Webb say: “Research productivity rises until the mid-1980s and then falls. Overall, between 1975 and 2006, research productivity for all cancers declines by a factor of 1.2 using all publications and a factor of 4.8 using clinical trials. The declines for breast cancer and heart disease are even larger.”
The same report also looks at Moore’s Law and says that the number of researchers required to maintain the pace of Moore’s Law is increasing.

The point that is overlooked

But Moore’s Law is an exponential concept. The above report says that since 1971 the number of researchers required to advance Moore’s Law has increased 18-fold. But computer power has increased one billion-fold since 1971.

Think of this way, one more rung on the Moore’s Law ladder, computer power doubles, for the number of researchers required to achieve this to increase in tandem, you would have to see that number equal the cumulative total of all researchers employed in that area up to date. That is a lot more than an 18-fold increase since 1971.

In fairness to the report’s authors, they do say: “In some ways a more accurate title for this paper would be ‘Is Exponential Growth Getting Harder to Achieve?"

But exponential is the crucial point. A computer that is twice as powerful as another computer can do things that the slower machine can’t do. There comes the point in computer advancement when you pass a kind of tipping point. Before that tipping point, certain things were impossible, after, they become possible.

But it is not just computers that have advanced. Computer advances have enabled us also to increase our knowledge of genetics, of genome sequencing, of DNA editing.

It is because of improvements in technology that Covid vaccines have been developed so quickly.

But it doesn’t stop there. Consider the following...


British AI company and Google subsidiary, DeepMind has applied AI to simulate protein folding — this means that we are close to understanding at the molecular level how amino acids form protein chains, which in turn make all life possible.

DeepMind’s AI solution is called AlphaFold. It’s just another member of the DeepMind family of AI tools — AlphaZero is the AI system that won the Chinese game of Go in a match against the best player in the world.

DeepMind said: “This breakthrough demonstrates the impact AI can have on scientific discovery and its potential to dramatically accelerate progress in some of the most fundamental fields that explain and shape our world.”

It cited Professor Venki Ramakrishnan, Nobel Laureate and President of The Royal Society as saying: “This computational work represents a stunning advance on the protein-folding problem, a 50-year-old grand challenge in biology. It has occurred decades before many people in the field would have predicted. It will be exciting to see the many ways in which it will fundamentally change biological research.”

Quantum computing

Before the DeepMind breakthrough, Quantum computers were considered the best hope of solving the problem of protein folding. I still think they will be vital in supporting the development of drugs that capitalise on our understanding of protein folding.
I won’t go into detail about quantum computing here — that’s not what this article is about — suffice to say that quantum computers build on the weird properties of quantum mechanics.

Instead of using bytes, like traditional computers, they use Qubits. Quantum computers are not merely advancing at an exponential rate; they are advancing at a rate which is an exponential of an exponential. Each additional Qubit leads to at least a doubling of processing power, and the number of Qubits are doubling every year or so. According to Rose’s Law, they are doubling every nine months.

Quantum computers will not be suitable for all tasks that traditional computers can do, but they will be faster at specific tasks, perhaps by a trillion-fold, or more.

I say all of this, because researchers from University of Science and Technology of China in central Hefei, in China, say they have built a quantum computer that can perform a specific calculation a 100 trillion times faster than a traditional computer could.

So, applying the logic that computers require more researchers to advance at the rate they used to advance at, for this to be true, there must be 100 trillion times more researchers at this Chinese university than the cumulative number of researchers employed in global chip design R&D since 1971. I know the Chinese population is large, but I don’t think it that big. And what are the implications of so-called quantum supremacy? They are as profound as you can imagine, but I would like to suggest that drug discovery will be among the beneficiaries.


The ability to edit DNA is another one of those technologies that is as profound as you can imagine— there are a growing number of them about, these days.

CRISPR is the technology applied in nature to edit DNA; cas9 is the protein that makes it possible. CRISPR/cas9 is often said to have been discovered in 2012 by Charpentier and Jennifer Doudna, University of California, Berkeley. That is a slight simplification; there was a long line of breakthroughs in its discovery. Even so, it is safe to say that the discoveries of Charpentier and Doudna, for which they recently won the Nobel Prize, was a pivotal moment.

A certain Vladimir Putin has said of DNA editing technology: “Genetic engineering will open-up incredible opportunities in pharmacology, altering the human genome.  If a person suffers from a genetic disease...that is good. But there is another part to this process. It means we can...create a person with desired features. This may be a mathematical genius. They may be an outstanding musician. But it may also be a soldier, an individual who can fight without fear, or pain. You are aware that humanity will probably enter a very complicated period of its existence and development. And what I have just said maybe more terrifying than a nuclear bomb.”

And yet, I read that CRISPR/cas9 technology has already been applied in the treatment of sickle cell disease.


As you know, we are in danger of losing the battle against superbugs. The overuse of antibiotics is killing their effectiveness. We desperately need to discover more powerful antibiotics. A significant breakthrough occurred in this area, earlier this year, using machine learning (the only application of AI in wide use today).

The new golden age

So that is a handful of examples. It may be that drug discovery has slowed in recent years, but I expect it to increase over the next two decades rapidly.

To use the terminology of Living in the age of the jerk (co-authored with Julien de Salaberry), technology is no longer accelerating, its acceleration is accelerating. Furthermore we are not seeing a smooth trend in innovation, instead it jerks, with new innovations opening up new possibilities.


Not all the technologies that are emerging in health science will benefit pharmaceutical companies. Some therapies may not require breakthrough new drugs; they might use other technologies. The CRISPR/cas9 treatment I referred to above-applied electroporation. We will also see nanotechnology applied, such as nanorobots. Furthermore, healthcare is going to mutate into shifting the emphasis from treatment to prevention.

Even so, I expect new technologies to support the development of new revolutionary drugs. As I have said here before, I am bullish about pharmaceuticals.

I am even more bullish about healthcare in general, and as for those who say the rate of innovation in healthcare is slowing, I would say, this may have been true for a while, but it won’t be true this decade and next.

Article originally posted on share.com

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