You would think the pharmaceutical industry was a profitable one. At first glance, it would seem so. In 2020, the drug industry worldwide pulled in 1.27 trillion US dollars in total revenue. US-based companies like Johnson & Johnson, Pfizer, and Lilly raked in the lion share at just over $56 billion combined.

Those numbers hide a slow, risky, and expensive development process, though. R&D for a new drug can cost from nearly a billion dollars to over two billion each. Those that show promise then have to go through clinical trials mandated by the FDA. These are also costly, with figures in phase 2 ranging between $7 million to $19 million, and jumping over $52 million in phase 3. (There are four phases.) On average, only 12-13 percent of new drugs navigate the entire 10-15 year long process and make it to market.  

As you can imagine, pharmaceutical companies are constantly looking for ways to speed up the process and reduce expenses. Three technological ones have caught their eye. Two of them, artificial intelligence (AI) and cloud computing, are readily available. Companies hope to use them to handle the massive amounts of data generated by R&D and the clinical trials. The third, quantum computing, is more theoretical. Industry leaders are keeping an eye on it for its game-changing potential. 

Artificial Intelligence: Sorting Data for Solutions

The information produced by drug companies can range from the make-up of a particular molecule to the survey results by trial volunteers. Much of it is stored raw in databases like data lakes. 

Artificial Intelligence is being tasked to search and sort through all that data. Topping the list is finding new drugs (or the possibilities to one). Those computers programmed for drug-discovery would, for example, have to figure out how a specific molecule works to produce a particular result in a drug. No specific program to reach the solution is provided; instead, the machine itself would have to come up with one on its own. Researchers can then use the newly generated algorithms to create new, similar drugs, and be confident with their effects.

Besides discovering new drugs, such machine intelligence can help determine their effects before testing on living things. One with deep-learning capability would first learn the different parts of cells after examining a million images. It would then apply the knowledge in discerning the differences between normal, healthy cells and abnormal ones. Such information could be useful in figuring out the effects of a new drug in combating cancer cells. 

Companies are even turning to such programs to assist in the all-important clinical trials. Studies have shown that around 80 percent have failed because the company could not find the right candidates to meet the trials’ deadlines. Computers with AI-capabilities could sort patient data obtained from an EMR and similar sources for the most well-qualified ones. This can drastically increase the success of trials throughout all four phases.

Managing chronic diseases to even predicting the next epidemic are also being considered for such advanced applications. 

Cloud Computing: Managing Data for Solutions

The data lakes mentioned earlier can be built and stored using cloud computing. By doing so, drug companies avoid the need for a costly in-house network and IT department for its management. This includes expensive hardware and software which can quickly become obsolete. 

Drug companies, freed from handling their current and future computing needs and costs, can scale operations as desired. Need more storage to house a new AI algorithm? Just notify the cloud provider. Need even more space as well as computational power to run said AI’s machine and deep learning programs? Again, contact the provider. Hyperscale cloud providers Amazon, Google, and Microsoft are just a few of the companies with networks large enough to handle many large drug companies’ operations. 

The widespread nature of cloud computing means drug companies can spread their tasks globally. Employees can work collaboratively from home or other remote locations, which became vital during the COVID-19 pandemic and the lockdowns that followed. Collaboration extends beyond the company, too. Partners like small biotech firms, academic groups, and even rival drug companies will be able log on 24/7 with their medical panel PC to work on mutual goals with minimal costs. Such cooperation cuts both costs and time on drug development and approval. 

Quantum Computing: Apply Physics to Drug Chemistry

Drug discovery, analytics, and testing generate massive amounts of information, and processing it all strains even the best supercomputers. Pharmaceutical companies are hoping quantum computing is the breakthrough they’ll need to deal with this bottleneck. 

Today’s computers, whether a medical box PC, a supercomputer, or a high-performance computing system (HPC), use “bits” that are either on or off to perform their calculations. Quantum computers, on the other hand, use qubits, which are basically bits that are both on and off at the same time in a state called “superposition.” This allows them to make two calculations at once. 

While impressive, it’s when the bizarre quantum property of entanglement comes in that quantum computers really come on their own. The computing power of conventional PCs increases linearly. In quantum computing, thanks to entanglement, processing rises exponentially: two qubits can do four calculations at the same time; three qubits do eight; four qubits solve sixteen; etc. As a way of comparison, it would take a quantum PC a split second to search for a particular item in a pile of one trillion. A similar, modern computer, spending a microsecond to check each item, would take a week. 

Drug companies are eyeing quantum computers to assist them in two distinct but related ways. The first is in drug discoveries. Predicting the interactions between 5 to 10 atoms of a particular molecule is considered the limit for supercomputers. However, many of the molecules considered for even small molecule drugs like aspirin have 30 to 40 atoms. Supercomputers could take months to years to perform the necessary calculations if they can do it at all. Quantum computers, on the other hand, would take seconds. This would drastically decrease both time and money spent in finding new drug molecules for commercial products. 

Current quantum computers are also limited to around 10 atoms as well. However, this is due to unique hardware and software issues, both of which are still in development. Once commercially available, they would drastically speed up many processes, especially drug-discovery. They could potentially reduce or even eliminate human trials thanks to in silico models, which uses “virtual humans” to calculate all the possible effects of a new drug on the human body.

Closing Thoughts

The process to bring new drugs to market is a risky one. Pharmaceutical companies are keenly interested in ways to cut costs, speed up the process, or both. AI, cloud computing, and quantum computers are currently being explored as solutions. Contact an expert at Cybernet if you’re interested in learning more about them and how they can be applied to your pharma pursuits.

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