New medicines’ research and development process is complex and lengthy and requires significant investments. The pharmaceutical industry finds it increasingly hard to develop and market efficient medication. The entire process can last up to 12 years and cost between 1.9 and 3.2 billion dollars, according to DiMasi et al., Journal of Health Economics, January 2016.

During clinical trials, doctors carry out complex biochemical and physicochemical procedures using massive data. The probability of a drug passing all tests is less than 12%, according to CSIC sources.

The pharmaceutical industry has dived into computing and artificial intelligence to improve this process.

Artificial intelligence in the pharmaceutical industry

The biopharmaceutical industry has been one of the key beneficiaries of artificial intelligence, mainly because it helps streamline the research and development of medicine and reduce research costs and the number of drugs undergoing clinical trials.

The pharmaceutical industry usually collaborates with hospitals and tech companies. That was the case during the COVID-19 pandemic, where the sector used AI algorithms to search for information, conduct studies, and carry out test simulations faster.

These algorithms helped identify the gene coding for target proteins that could favorably bind to drugs, reducing the initial drug development phase from around five years to only a couple of months.

These AI-powered techniques have played a vital role in developing COVID-19 vaccines with companies such as AstraZeneca or Janssen. Likewise, AI has made it easier to predict how the pandemic might continue to spread and which mistakes we can avoid if we encounter a similar situation in the future.

The use of Big Data and AI in the healthcare industry is quite positive, and it impacts both pharma companies and society as a whole. That’s why many organizations have begun to bet on this technology.

The use of AI in drug research

Artificial intelligence in the biopharmaceutical industry has revolutionized the sector by facilitating access to biometric data in an agile way and helping identify hidden patterns.

AI may also prove helpful in these cases:

Patient segmentation

Proper patient segmentation is one of the biggest challenges in clinical trials. Choosing patients according to eligibility, suitability, motivation, and empowerment is crucial, and these processes usually involve significant delays that can be solved with AI.

Thanks to these mechanisms, a more efficient patient classification is possible, and appropriate treatments can be promptly administered, anticipating potential risks and getting more favorable results.

Disease prediction

It’s possible to automate medical record screening or predict behavior patterns to understand how patients react to specific treatments or identify the reasons behind their hospital readmission.

Resource optimization

With artificial intelligence, hospitals and healthcare institutions can reduce the number of in-person visits and replace them with online appointments. AI also allows healthcare professionals to monitor patients, regardless of whether they visit the healthcare center in person or not.

Artificial vision

Radiologists can also benefit from these innovations. Artificial intelligence facilitates faster image diagnoses, streamlining the initial stage of treatments.

Pharmaceutical compound research

AI can automate complex tasks and save time.

During the initial stages of medicine research, AI significantly facilitates cellular trial analysis and molecular structure modeling, as well as the prediction of physicochemical properties of compounds, among other tasks.

Artificial intelligence also helps understand the possible interaction between proteins and ligands, allowing for the creation of molecules with more significant potential.

It’s also helpful when it comes to identifying and differentiating images, probably two of the least efficient processes in the development of medicine. Learning machines can spot the tiny differences in cellular structures from microscopic images.

AI not only streamlines the initial stages of the process. It also proves valuable for quality control and optimizing medication administration.

Drug reuse or repositioning

Drug reuse is a strategy that seeks to discover new uses for medication that has already been approved.

That’s the case with Botox, for instance. It was initially created as a treatment for strabismus, but it was later discovered that it could also help with migraines and remove wrinkles.

Reusing drugs involves fewer risks and streamlines the development process. However, effectively combining clinical trials can be expensive and time-consuming.

In this context, artificial intelligence can develop hypotheses faster, accelerating clinical trials.

 

Advantages of implementing artificial intelligence

Artificial intelligence benefits significantly by predicting how molecules might react to certain medications.

It reduces the failure rate of clinical trials

Drugs in clinical trials have a success rate of under 12%. The reasons behind that figure include the lack of efficiency, adverse effects on humans, and the high costs of resources, all of which hampers the commercialization of the product.

Thanks to advanced artificial intelligence technology, the success rate of molecules can be increased, thus reducing the medicine’s adverse effects on humans. Machine learning algorithms can also improve efficiency without compromising security.

It accelerates the research of new medicines while reducing costs

Right now, the drug development process can last up to 12 years, including the preclinical and clinical stages, and it costs more than 2 billion dollars.

As new issues arise in different stages of the development process, the price continues to go up, especially during phase 3, where biomarkers differentiate drug responses.

Only the search for the compound —which involves target and lead selection, validation, and optimization— can last between 4 and 6 years. And at this point, only 1% of compounds reach the following phase.

That’s why the use of artificial intelligence and machine learning at the beginning of the process is crucial to reduce the risk of failure. A streamlined research and development process helps save time and resources.

It helps identify innovative treatments

Thanks to machine learning, technological advances and innovation in the pharmaceutical field have skyrocketed.

One of the critical developments powered by artificial intelligence involves personalizing treatments to adjust to each patient’s specific needs.

How companies are using AI

Many companies are already implementing artificial intelligence to save time and money in drug development.

Although research professionals are still vital in these processes, machine learning can significantly benefit pharma companies.

Moderna

Moderna is an excellent example of artificial intelligence implementation. During the COVID-19 crisis, many wondered how the company manufactured the vaccine so much faster than usual.

This achievement was due to Moderna’s use of state-of-the-art software and algorithms.

Exscientia and Sumitomo Dainippon Pharma

This case is one of the most striking ones, as it’s the first AI-created drug tested by humans. This is the case of molecule DSP-1181, which doctors developed with the help of AI as a long-acting serotonin 5-HT1A receptor antagonist, a drug used for the treatment of obsessive-compulsive disorder.

The development of this molecule was possible thanks to automated learning processes that allowed algorithms to generate millions of molecules and filter them to get the right one.

Deep Genomics

This Canadian company used artificial intelligence to identify the genetic cause of some diseases. Thanks to AI, it’s possible to develop drugs that regulate the defective genes causing these diseases.

Many pharmaceutical companies have already joined the digitalization era and are betting on artificial intelligence. While it’s still too early to know its future impact on drugs, the current results are satisfactory.

Leave a Reply

Your email address will not be published.

You may use these HTML tags and attributes:

<a href="" title=""> <abbr title=""> <acronym title=""> <b> <blockquote cite=""> <cite> <code> <del datetime=""> <em> <i> <q cite=""> <s> <strike> <strong>