4 Key Artificial Intelligence & Machine Learning Trends In Pharma

The pharmaceutical industry is one of the fastest-growing industries in the world and is expected to be a $1.5 trillion economy by 20231.

To meet the growth, companies are adopting more efficient & automated processes to propel data-driven decision making. This is leading to the development of new drugs, an increase in efficacy of existing drugs, combating fast-growing diseases, and quicker reach to the market.

By utilizing machine learning & artificial intelligence in the drug discovery and development phases, pharma is making strides in precision medicine to provide the right healthcare treatment to the right patient at the right time. From early-stage drug discovery to better comprehension of clinical trial data, the use of AI is growing steadily within the industry.

Here Are Four AI & ML Trends in the Pharma Industry

Drug Discovery and Development

The time and cost to bring a new drug from concept to market is 7-10 years and costs $2 billion2.

Additionally, the process from discovery to market is often met with high failure rates at various stages in the drug development process. The collaboration between Cyclica & Bayer to accelerate drug discovery and the work of Verge genomics to simplify the clinical development process are just a few companies that have been focusing efforts in this sector.

Cyclica & Bayer

Cyclica, a biotechnology firm, partnered with Bayer to advance drug discovery through a holistic set of AI & computational biophysics products by screening small molecule drugs against pre-existing repositories of proteins. The algorithms, working with protein and drug efficacy data, predict ADMET (Absorption, Distribution, Metabolism, Excretion, and Toxicity) (Lehrer, 2019) to drastically reduce discovery time.

Verge Genomics

Three pharmaceutical firms and several tissue banks have partnered with Verge Genomics, a neuroscience firm, to bring AI-generated compounds to the market to treat neurodegenerative diseases. Verge builds large databases3 of patient tissues, capturing human disease complexity and through AI, and combines the data with human genetics to discover novel gene targets.

Rare Diseases & Personalized Medicine

Pharma companies generally have focused their research efforts on diseases that affect large segments of the population. The regulatory compliance needs, long drug development times, expensive clinical trials, and the need to keep drugs affordable contributed to the industry’s focus on releasing a few blockbuster drugs.

In the last ten years, with the advent of AI and machine learning, pharma companies have shown more interest in developing drugs for rare diseases4. The partnership between Tencent Holdings and Huma showcases the power of AI in the detection and diagnosis of certain rare neurological diseases.

Additionally, algorithms are being used to develop personalized drug treatments based on an individual’s genes, environment, and lifestyle. Drugs are being personalized on composition, quantity, frequency, and method of administration. The joint effort between Aprecia and Cycle Pharmaceuticals focused on the development of orphan drugs.

Tencent Holdings and Huma

Technology conglomerate Tencent Holdings, in collaboration with healthcare firm Huma, is creating an AI platform to use motion capture technology to track patient movements. Deep learning, image recognition & video analysis are used both to evaluate the severity of a patient’s condition and significantly reduce motor test time.

Aprecia and Cycle Pharmaceuticals

Aprecia manufactures the epilepsy drug, Spritam, that is easy to administer by caregivers. Combining the precision of 3D printing and formulation science, the technology produces a tablet that disintegrates with a sip of water in just a few seconds. The company later partnered with Cycle Pharmaceuticals5, to grow and commercialize orphan drugs those greatly improved patient quality-of-life.

Clinical Trials

A CBInsights6 study showed that 80% of clinical trials fail to meet enrollment timelines and 1/3 of phase 3 trials terminate due to enrollment difficulties.

Patients find the national registry difficult to navigate and clinical trial staff must carefully search through patient health information to see if a candidate could be a fit. Two start-ups, Deep6 and Antidote, are solving the problems by using machine learning algorithms to parse, analyze, and interpret health data thus streamlining the patient selection process.

Deep6

Deep6’s AI software analyzes both structured and unstructured data such as ICD-10 codes, doctor’s notes, and medical reports. NLP algorithms extract new clinical data points from the data and researchers filter results for specific conditions and traits. The algorithm also makes predictions about the condition of a person based on extracted data, marking that person a potential candidate thus eliminating the enrollment difficulties for pharmaceutical firms.

Antidote

Antidote utilizes its AI platform to match patients and trials, rather than focusing on recruiting patients to a clinical trial. Working in conjunction with healthcare organizations such as JRDF and Lung Cancer Alliance, they serve people searching for a clinical trial based on their health conditions. Patients interested in a trial are surveyed on their medical history and based on the responses, Antidote’s algorithm categorizes7 and identifies clinical trials most suitable for the patient.

Drug Adherence and Dosage

Once a drug has reached the clinical trial stage, the next step is to prove the success rate of the drug in the study – adherence, and dosage are both crucial at this phase8: patients must follow rules to remain in the trial. Most trials use a combination of pill counts and self-reported data to measure adherence and dosage, but timing is often imprecise.

AbbVie & AiCure are using app-based smartphone technology with machine learning algorithms for better patient adherence. For diseases those are treated with a combination of drugs, Curate.Ai smart platform uses patient health information to identify the accurate dosage.

AbbVie & AiCure

AbbVie Pharmaceuticals partnered with AiCure to monitor drug adherence in schizophrenia patients through facial and image recognition algorithms. Participants record swallowing their pills via smartphone and the AI-powered platform confirms that the intended person took the correct pill at the right time.

Curate.Ai

Using the Curate.Ai platform, clinicians use a personalized medicine approach to find an appropriate drug combination along with a dosing strategy over time based on the individual’s data. This is more relevant in the treatment of diseases like cancer, where multiple drugs are administered simultaneously to the patients. Studies found this approach to be superior when compared to the drug and dosage decisions based on clinical trials representing a sample population.

These are only a few of the AI & ML trends benefitting the pharma industry. AI has the capacity to dive into the data, identify patterns, and generate accurate hypotheses quickly making the process less expensive and more efficient.

The drug development process – from discovery to market – used to take several years and cost billions of dollars but is now considerably streamlined with the implementation of smart technology. With new and improved innovations & deeper collaborations between technology firms and pharmaceutical companies – AI has the power to influence every aspect of the industry.

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References

1: https://www.pharmaceuticalcommerce.com/business-and-finance/global-pharma-spending-will-hit-1-5-trillion-in-2023-says-iqvia/

2: https://www.businessinsider.com/drug-costs-to-develop-a-new-cancer-drug-2017-9?utm\_source=Triggermail&utm\_medium=email&utm\_campaign=Post%20Blast%20%28bii-digital-health%29:%20IBM%20shelves%20Watson%20AI%20tool%20for%20drug%20discovery%3B%20Babylon%20gears%20up%20for%20US%20expansion%3B%20Telepsychiatry%20minimizes%20ED%20wait%20times&utm\_term=BII%20List%20Digital%20Health%20ALL

3: https://www.fiercebiotech.com/medtech/verge-genomics-gathers-32m-to-trial-its-ai-built-neuroscience-drugs

4: https://healthcareweekly.com/artificial-intelligence-in-pharmacology/

5: https://www.aprecia.com/technology

6: https://www.cbinsights.com/reports/CB-Insights\_Future-Clinical-Trials-Briefing.pdf

7: https://www.wired.com/story/meet-the-company-trying-to-democratize-clinical-trials-with-ai/

8: https://blog.umetrics.com/the-trending-role-of-artificial-intelligence-in-the-pharmaceutical-industry

Author Bio: Harika is an aspiring data scientist and holds a master’s degree in biomedical informatics with a focus in data science. She’s passionate about all things data but particularly, machine learning and artificial intelligence. Outside the data world, she’s a content writer in the areas of science, education, and health. Feel free to connect on LinkedIn.(https://www.linkedin.com/in/harikapanuganty/)