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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.

Last year, Verdict AI asked businesses how vital artificial intelligence will be in their respective industries and over 70% of them thought it would be very important. From the same group, only 11% of businesses have not considered investing in AI technology.

Furthermore, according to Narrative Science, 61% of companies investing in innovative strategies are using AI to identify opportunities that they would have otherwise missed. For pharmaceutical businesses that thrive on innovation, this is an important statistic to understand.

This article aims to help business executives learn what to expect from artificial intelligence in pharma. It will cover:

Artificial intelligence and pharma can help save more lives than ever before.

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.

Novartisis embracing advancements in AI technology to create new and improved treatments and find ways to get people access to treatment quickly.

Novartis is currently using machine learning to classify digital images of cells, each treated with different experimental compounds. The machine learning algorithms collect and group compounds that have similar effects together, before passing on the clean data to researchers who can decide how to leverage these insights in their work.

Drug discovery often takes a long time to test compounds against samples of diseased cells. Finding compounds that are biologically active and are worth investigating further requires even more analysis.

To speed up this screening process, Novartis research teams use images from machine learning algorithms to predict which untested compounds might be worth exploring in more details.

As computers are far quicker compared to traditional human analysis and laboratory experiments in uncovering new data sets, new and effective drugs can be made available sooner, while also reducing the operational costs associated with the manual investigation of each compound.

But theres another reason why Novartis is at the top of our list. CEO,Vas Narasimhan is one of the forward-looking digital leaders in healthcare who is constantly advocating for the role AI, predictive analytics and big data can play in Pharma.David Shaywitz, in an excellent Forbes articlesummarizes all the challenges Novartis is facing in adopting AI but also how the company is still pursuing AI with some notable results in clinical trials and finance.

Verge Genomics develops drugs by automating their discovery process. They use automated data gathering and analysis to create solutions to some of the most complex diseases known today, including ALS and Alzheimers.

Cost aside, one of the reasons why drug discoveries fail is because they only target one disease gene at a time.

Using the same technologies that power Googles search engines, Verge has discovered ways to map out the hundreds of genes responsible for causing disease and then finding drugs that target them all at once.

Their platform is specifically designed for neurological diseases and can predict the effect of new treatments, while also reducing the cost of drug development.

Bayer and Merck & Co were granted the Breakthrough Device Designation from the FDA for artificial intelligence software that aims to support clinical decision making of chronic thromboembolic pulmonary hypertension (CTEPH).

This form of pulmonary hypertension affects around five people per million, per year around the world. Its symptoms are similar to conditions like asthma and COPD, meaning it can be tricky to accurately diagnose.

The aim of the software is to help radiologists detect certain patterns faster, who are often on the frontline for identifying CTEPH patients. The AI would analyze image findings from cardiac, lung perfusion, and pulmonary vessels in combination with a patients clinical history and then pass the insights to the radiologists leveraging this technology.

Both Bayer and Merck note that the development of their CTEPH Pattern Recognition Artificial Intelligence Software remains complex due to the nature of the disease they are attempting to better diagnose.

However, should it prove successful, the tool will eventually be able to assist in diagnosing patients earlier and more reliably, leading to earlier treatment and better patient outcomes.

Cyclica is a biotechnology company that combines biophysics and AI to discover drugs faster, safer, and cheaper. They have partnered with Bayer to create an AI-augmented integrated network of cloud-based technologies, known as the Ligand Express.

The Ligand Express screens small-molecule drugs against repositories of structurally-characterized proteins to determine polypharmacological profiles. From here, the company identifies significant protein targets and then they use artificial intelligence to determine the drugs effect on these targets. Finally, the AI produces a visual output of how the drug and proteins interact.

By understanding how small-molecule drugs interact with all proteins in the body, Ligand Express can produce the best solution, understand potential side effects, and determine new uses for existing drugs.

AI in pharmacology can also be used to find cures for known diseases such as Parkinsons and Alzheimers, 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 dont focus their efforts on treatments for rare diseases because the return on investment doesnt 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.

Tencent Holdings has partnered with UK-based Medopad to build artificial intelligence algorithms capable of remotely monitoring patients with Parkinsons disease and reducing how long it takes to conduct a motor function assessment from over 30 minutes to less than three minutes.

The AI will leverage smartphone apps that monitor how a patient opens and closes their hands. The smartphones camera captures a patients movement to determine the severity of their symptoms. The frequency and amplitude score the patient receives can determine the severity of their Parkinsons.

This will allow doctors to remotely monitor patients and set new drug doses. If a patients treatment program needs changing, the AI will raise an alert to notify their doctor and arrange a checkup if required.

The technology will also reduce the patients costs of traveling back and forth to the clinic.

Mission Therapeutics, a drug creation company known for its chemistry and proprietary enzyme platform, and AbbVie, a pharmaceutical business known for its strong neurodegenerative disease research, have partnered to develop Deubiquitinase (DUB) inhibitors in the fight against Parkinsons and Alzheimers.

Both Alzheimers and Parkinsons patients have an abnormal accumulation of misfolded, toxic proteins, resulting in impaired brain functionality and the death of nerve cells.This is where DUBs comes in. They regulate the degradation of these proteins to maintain their health and stability.

By modulating specific DUBs within the brain, Mission Therapeutics is aiming to find potential treatments which will enable the degradation of these toxic proteins and prevent their accumulation.

Healx is a promising startup focused on accelerating treatments for rare diseases and artificial intelligence is at the center of their operations. Their AI platform HealNet enables scientists to increase production in disease drug discovery while simultaneously reducing time, cost and risk.

The company isnt directly focused on creating new drugs to cure these conditions. Instead, they use AI technology to examine existing drugs and repurpose them for curing rare diseases.

HealNet uses machine learning techniques to access data from a range of sources, including scientific literature, patents, clinical trials, disease symptoms, drug targets, multiomics data and chemical structures.

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 dont follow the trial rules, they are eitherremoved 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, its been reported that machine learning algorithms can cut incorrect drug dosage intake by as much as 50% for glioblastoma patients.

Traditional methods to measure drug adherence require patients to submit the data themselves without any evidence of them taking a pill or other type of treatment. They are also subject to tampering, such as deceptively removing pills to feign higher adherence.

AiCure, a New York-based mobile SaaS platform, has developed an image recognition algorithm that removes these issues. Using a mobile phone, AiCure tracks drug adherence by videoing the patient swallowing a pill. The facial recognition system then confirms that the right person took the right pill.

In 2016, they published findings from their study that confirms that the use of their AI platform significantly increases adherence in patients with schizophrenia, as measured by drug concentration levels.The results showed that cumulative adherence was at 89.7% for those using the AiCure platform compared to 71.9% for subjects using modified Directly Observed Therapy (mDOT).

Even with the obvious advantage that this brings, AI will also decrease costs and accelerate drug development for clinical research and practices.

A research team led by the National University of Singapore (NUS) has used an AI platform called CURATE.AI to successfully treat a patient with advanced cancer and completely halting disease progression.

In this clinical study, a patient with metastatic castration-resistant prostate cancer (MCRPC) was given a novel drug combination consisting of an investigational drug, namely ZEN-3694, and an already-approved prostate cancer drug, enzalutamide.

CURATE.AI was used by the research team to continuously identify the optimal doses of each drug to result in a durable response, giving each individual patient the ability to live a free and healthy life.

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Artificial Intelligence & the Pharma Industry: What's Next ...