The pharma industry thrives on innovations and is adopting emerging technologies to stay ahead of the curve. The use of AI in pharma has an immense potential to increase efficiency, cut costs, improve quality, and save more lives than ever before.
In this article, we will talk about the impact of AI on pharma and biotech industries, explore some of the most prominent use cases and talk about how pharmaceutical companies can win by embracing digital transformation.
3 Most Prominent Applications of AI in Pharma
Over the last five years, AI has been finding increasingly vast applications in pharma. More specifically, technologies like robotic process automation, machine learning, computer vision, image recognition, and big data analytics are now revolutionizing areas previously dominated by paperwork and manual labor.
So what are the most prominent AI in pharma use cases? Let’s explore.
AI in Drug Discovery
New drug discovery is usually a lengthy and costly process, with an extremely low success rate. Even if you go through all the steps involved in pharmaceutical R&D (which may take years!), the drug may not get clinical approval. When it comes to drug discovery, AI can automate and accelerate a number of critical processes, such as:
– Identifying drug targets and lead structures
– Selection of lead components
– Predicting outcomes of lab experiments based on data and advanced analytics, etc.
This enables companies to reduce the sample size of clinical trials, accelerate clinical testing, and increase the rate of clinical approvals for new drugs.
Use Case: Novartis, one of the global pharma leaders, is currently using AI for new drug development. Novartis is leveraging machine learning and image recognition technologies to categorize images of diseased cells each treated with different compounds and forwards them to researchers for further analysis. This helps the company streamline a vast portion of the R&D routine and accelerate the discovery of new drugs.
Rare and Chronic Disease Prevention
AI helps find treatments for diseases previously deemed incurable, such as Parkinson’s and Alzheimer’s, and also to find cures for rare diseases. Developing drugs for rare diseases has always been unprofitable for pharma companies since it requires lots of investment with uncertain outcomes. Presently, though, the use of AI has the potential to make a true breakthrough in this area.
Use Case: Mission Therapeutics is a project targeted at fighting Parkinson’s and Alzheimer’s diseases. The project focuses on developing Deubiquitinase (DUB) inhibitors regulating the degradation of toxic proteins that are damaging brain cells. Using artificial intelligence, the project will modulate DUBs within the human brain and search for potential treatments.
Clinical Data Analysis
Clinical testing is one of the current pains of the pharma industry. Cognizant research has revealed that one-third of clinical tests get terminated at the late stages because of enrollment difficulties, and almost 80% of tests fail to meet deadlines. This is mostly because the patients still input their data manually, and because the data has to undergo the lengthy process of extraction and analysis.
AI can assist in collecting, extracting, and analyzing data thus streamlining clinical data analysis and increasing the efficiency of clinical tests. Moreover, artificial intelligence can also help to find patients for conducting new drug trials.
Use Case: IBM Watson analyses the structured and unstructured data from the patients’ records and matches it with information about clinical trials. By leveraging natural language processing (NLP) and big data analytics the system can read the medical records on the patients’ symptoms and health conditions and determine if a patient in question is eligible for a particular test.
Benefits of Using AI in Pharma
About 61% of businesses worldwide have strategies that involve the use of AI to optimize operations and increase revenues. This includes pharma companies, who recognize the value of using AI for all facets of their business, from pharmaceutical R&D to logistics and sales. The benefits of artificial intelligence in pharma include:
∎ Accelerating Drug Discovery
In a complex field of pharmaceutical R&D, AI and nascent technologies help to automate and streamline a range of repetitive, rule-based processes, like filtering out suitable ingredients from a large number of compounds, identifying drug targets, collecting and making sense of data for clinical tests, etc.
As of today, laboratory management systems like LIMSys, improve the efficiency of lab operations by cutting down on manual tasks and can be also used in pharma R&D to accelerate and streamline the drug discovery and quality assurance in pharmaceutical compounding.
In the pharma industry, where new drug testing and development may take up to 15 years and the rate of successful clinical testing is as low as 1%, this is an important advantage that helps to reduce time-to-market.
∎ Improving the Quality of Pharmaceuticals
Traditionally, the discovery of new drugs has involved trial and error, manual data collection, and a lot of guesswork. The new, AI-driven approach will be based on recognizing the sophisticated patterns and matching the relevant treatments with the patient’s data. This has an immense potential to improve the quality and efficiency of the new drugs. In the future, the use of AI in drug discovery could enable the shift towards personalized medicine.
Berg, a biotech company from Boston, MA, has used AI to test samples of more than 1000 of human healthy and cancerous cells to identify previously undiscovered cancer mechanisms. Further, Berg’s researchers are using AI to detect the key differences between healthy and infected cells. The company aims to find cancer treatments that target the individual biological causes of disease.
The company has now completed Phase 2 of its new BPM31510 drug for treating pancreatic cancer discovered using Berg’s Interrogative Biology platform.
∎Cutting Costs and Increasing Revenue
As of today, the estimated cost of developing a new medicine is about US$2.6 billion. Optimizing the number of lab personnel by deploying automated lab management systems, streamlining routine processes in pharmaceutical R&D not only accelerates new drug discovery but also reduces the expenses that come with it.
By predicting the outcome of clinical tests, AI can reduce their number and increase the rate of clinical approvals. This helps pharma companies cut the R&D costs and increase revenue.
On top of that, AI is equally helpful in areas that are not directly related to R&D, such as automating pharma production and financial operations, logistics, warehousing, and marketing. For example, pharmaceutical manufacturing can leverage IoT to monitor the state of equipment and schedule timely repairs to avoid downtime and disruptions.
Yet, the adopting AI in pharma is still associated with many challenges and the industry is still a far cry from unleashing its full potential. As Novartis CEO Vas Narasimhan points out, it may take years just to collect and clean your data sets, before you can actually use it for your machine learning project. Yet, clinical trials and using medical image analysis for drug discovery are areas where we can expect major breakthroughs.
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