Artificial intelligence in Pharma refers to the use of automated algorithms to perform tasks which traditionally rely on human intelligence. Over the last five years, the use of artificial intelligence in the pharma and biotech industry has redefined how scientists develop new drugs, tackle disease, and more.
Given the growing importance of Artificial Intelligence for the pharma industry, we wanted to create a comprehensive report which helps every business leader understand the biggest breakthroughs in the biotech space which are assisted by the deployment of artificial intelligence technologies.
This article aims to help business executives learn what to expect from artificial intelligence in pharma. It will cover:
- The role of AI in developing new drugs
- How AI can tackle diseases previously deemed too difficult to take on
- AI and drug adherence
- The use of AI to make sense of clinical data
- How AI can help find the correct patients for clinical trials
Artificial intelligence and pharma can help save more lives than ever before.
1. Developing new drugs
A study published by the Massachusetts Institute of Technology (MIT) has found that only 13.8% of drugs successfully pass clinical trials. Furthermore, a company can expect to pay between $161 million to $2 billion for any drug to complete the entire clinical trials process and get FDA approval. With this in mind, pharma businesses are using AI to increase the success rates of new drugs while decreasing operational costs at the same time.
Some Important Related AI Projects – Existing and Upcoming
- Novartis uses AI to predict untested components researchers should explore to find new cures
- Verge Genomics uses AI to predict the effect of new treatments for patients suffering from ALS & Alzheimer’s
- Bayer and Merck & Co uses AI algorithms to identify pulmonary hypertension
- Cyclica & Bayer use AI to determine determine polypharmacological profiles faster & developer more affordable drugs
2. The use of artificial intelligence in pharma to tackle diseases previously deemed too difficult to take on
AI in pharmacology can also be used to find cures for known diseases such as Parkinson’s and Alzheimer’s, as well as rare diseases. This is great news considering the fact that 95% of rare diseases do not have a single FDA approved treatment, according to Global Genes. Traditionally, pharmaceutical companies don’t focus their efforts on treatments for rare diseases because the return on investment doesn’t warrant the time and cost it takes to produce the drugs.
Traditionally, pharmaceutical companies don’t focus their efforts on treatments for rare diseases because the return on investment doesn’t warrant the time and cost it takes to produce the drugs. However, with advancements in AI technology, there has been a renewed interest in rare disease treatments.
Some Important Related AI Projects – Existing and Upcoming
- Tencent Holdings leverages AI to remotely monitor patients with Parkinson’s
- Mission Therapeutics uses AI to develop treatments for Alzheimer’s
- Healx uses AI to help biotech companies find treatments for rare diseases
3. Drug adherence and dosage
Drug adherence is huge for pharma. In simple terms, to prove the success rate of a drug, a pharma company uses voluntary participants in clinical studies. If these patients don’t follow the trail rules, they are either removed from the trial or they poison the drug results. As a result, having amazing drug adherence is crucial to any pharma company out there.
Another critical component for a successful drug trial is that participants take the necessary dosage of a particular drug at all times. For example, it’s been reported that machine learning algorithms can cut incorrect drug dosage intake by as much as 50% for glioblastoma patients.
Some Important Related AI Projects – Existing and Upcoming
- AiCure And AbbVie use image recognition to improve drug adherence
- CURATE.AI built an AI platform to halt disease progression by optimizing drug dosage at an individual level
- AstraZeneca and Alibaba build AI to help patients with automated cancer diagnostics
4. Using AI to make sense of clinical data and to produce better analytics
Clinical studies are still reliant on paper diaries instead of using modern, electronic systems. Patients are required to note the time they took a drug, record any other medication they took, and describe any adverse reactions they experienced. When it comes to the trial sites themselves, many biotech companies are still using fax machines to request and receive patient records from hospitals and have to manually extract relevant information from them.
When it comes to the trial sites themselves, many biotech companies are still using fax machines to request and receive patient records. A Cognizant study showed that around 80% of clinical trials fail to meet enrollment timelines, and one-third of all Phase III clinical study terminations are due to enrollment difficulties. Extracting and making sense of clinical data from medical records is highly sought-after in the medical and pharma industries and AI can be the answer to it.
Some Important Related AI Projects – Existing and Upcoming
- IBM Watson helps match patients with the right drug trials
- Apple uses AI to screen children for autism
- GNS Healthcare and Genentech use AI to develop new cancer therapies
5. Artificial intelligence helps pharma companies find patients for clinical trials
While AI can be used to make sense of clinical trials data, another use of artificial intelligence in the pharmaceutical industry is to find the patients to take them. Companies like Deep 6 AI apply artificial intelligence to medical records to find more, better-matching patients for clinical trials in minutes, rather than months. Deep 6 AI’s software analyzes data like doctor’s notes, pathology reports and operational notes that are not easily found. With the use of AI, companies can compile data from these important documents to better predict which patients will respond favorably to the drug or device in question and have a higher chance of sticking with the trial.
Some Important Related AI Projects – Existing and Upcoming
- Antidote uses Natural Language Processing to screen patients for drug trial enrollment
- Deep 6 uses AI to proactively find drug trial candidates
- Santen and twoXAR are using AI to develop drugs for glaucoma
Future Outlook
Even with all the benefits that Artificial Intelligence has already brought to the pharmaceutical industry, a report by the HIMSS Analytics 2017 Essentials Brief shows that less than 5% of healthcare organisations are currently using or investing in AI technologies.
Most pharma companies’ current IT infrastructure is based on legacy systems that were not designed with AI in mind. They lack sufficient data storing and often lack interoperability. The majority of data within medical systems is in free form so until systems like Deep 6 and Antidote are available, the information cannot be processed and used efficiently by health professionals. Finally, machine learning and smart automations are still seen as a relatively new technology, even though both have been available for a while.
However, with more information provided to the decision makers (such as through this article!), those in a position to influence organizational decisions around AI will hopefully get the ammunition they need to lead their orgs into the future. AI is the future of pharma but the technology is available now.
References
- AI is the Future of Pharma but The Technology is Available Now – https://www.digitalauthority.me/resources/artificial-intelligence-pharma/
- Companies like Deep 6 AI apply artificial intelligence to medical records to find more, better-matching patients for clinical trials – https://www.pharmaout.com/2021/06/04/artificial-intelligence-in-the-pharma-industry-clinical-trials/
