Artificial intelligence (AI) is no longer the stuff of science fiction. Many technologies today are now using AI technologies as part of their operations, from facial and voice recognition software to autonomous machines that help with manual tasks. AI is also now being used in the biotechnology and pharmaceutical industries, where it has become an important tool in drug discovery and development.
AI offers several applications in biotech such as image screening, drug identification, and predictive modeling. AI can also do careful cross-referencing of several scientific literature as well as manage clinical trial data.
Through its machine-learning capabilities, AI can also assist in collating, sorting, and analyzing large and diverse amounts of data. This helps in reducing clinical trial costs as well as provide insights that were otherwise difficult to obtain during the drug development process.
Hundreds of optimization problems in the pharmaceutical and biotech industries can now be resolved by modern AI techniques. Here are just a few:
AI may require billions of dollars and years of research but the potential applications are diverse and could help in exploring new ways of developing new drugs and with clinical trials. With this potential in mind and with the hope that the new technology may help with developing more effective drugs, pharmaceutical companies are now either investing in their own in-house AI development teams or collaborating with AI companies.
Exscientia is one of the few companies that have dedicated themselves to creating AI-based strategies for drug discovery and development. One of these strategies is the creation and development of tools that help identify small-molecule drug candidates at a faster rate. Another company, Recursion Pharmaceuticals, has raised $436 million in its initial public offering and has generated vast amounts of bespoke data on cellular behavior that can be used by AI machines to provide valuable biological information that could be used in discovering new drugs. Other established tech companies such as IBM, Microsoft, and Google are now venturing into the healthcare realms as well with the same goals.
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According to a study published on clinical development success rates published by the Biotechnology Innovation Organization, 9 out of 10 clinical drugs fail before the trials, and many of those that pass in phase 1 of the trials do not make it to phase 2. And even when they pass the trials, many still do not get approved by a regulatory body for market release due to being unsafe or ineffective. This makes drug development very expensive. Developing a new drug can cost from about $10 million to $2 billion. It can also be quite time-consuming and the odds of a new drug to not be put into mass production can be very high.
Predictive analytics is one of the most common and important applications of machine learning technologies. It employs a variety of statistical techniques such as data mining and predictive modelling in interpreting historical and current facts to make predictions and forecasts.
In the context of pharmaceuticals, predictive analytics is often applied in drug development. AI has the key to transform clinical trial design from preparation to execution ensuring improved success rates and removing obstacles for research and development teams. Some companies have also implemented AI to predict which potential medicines will work and which ones will not, based on data on molecular structures.
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Subroto Mukherjee, Head of Innovation and Emerging Technology, Americas at GlaxoSmithkline Consumer Healthcare, says, “We have popular seasonal brands in the Allergy and Cold and Flu category. The business use case is to have a predictive model that predicts how the upcoming season for allergy or cold and flu would shape up in different regions, and when the predicted peaks and troughs would be.”
Mukherjee says that the advantage of having the information they produce from predictive forecasting is for them to inform consumers on their websites about the best times to buy their products, improve national and regional media delivery, and inform their retailers about the best seasons to distribute, stock up, and display their products.
Manufacturing process improvement
AI also provides several opportunities to improve processes in development and production. It can optimize quality control procedures, shorten the design period, reduce waste, improve the reuse of materials, perform predictive maintenance, among others.
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One example of an AI application in manufacturing is through the use of Computer Numerical Control (CNC) machines. These machines can autonomously input or manage data that would otherwise normally require human intervention. Machine learning algorithms not only perform tasks with utmost precision, they can also proactively look for areas that need to be improved or streamlined. Hence, production becomes faster, less wasteful, and more consistent with regulations, especially when assessing the products’ Critical Quality Attributes.
The image processing capabilities of AI can also help in quality control of packaging and included items such as printed instructions and dose applicators. Some frequent problems such as counting the number of pills in a bottle, inspecting the sizes and shape of each pill, and checking for damages can be accurately controlled through image analysis.
Processing biomedical and clinical data
Many sophisticated AI technologies today are designed to read, sort, and interpret large volumes of textual data. This helps save a lot of time for researchers in the pharmaceutical and medical fields as it helps validate or discard hypotheses more efficiently without having to read through enormous amounts of data and decades worth of research publications.
Many clinical studies still make use of paper diaries that are given to patients to log the times when they took a prescribed drug or other medications that they might have taken, as well as any reactions that they experience. AI can collect and interpret these handwritten notes along with other clinical data and test results. Furthermore, researchers can benefit from data as it provides them with a faster way of cross-referencing data and combining and extracting these into usable formats for analysis.
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