Unlocking the Future: 4 Key AI and Machine Learning Trends Reshaping Pharma
Harika Panuganty | July 8, 2023
The pharmaceutical industry is one of the fastest-growing industries in the world and is expected to be a $1.5 trillion economy by 2023.
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.
AI in Pharma is revolutionizing the landscape of drug discovery and development. It's enabling pharma companies to advance precision medicine, ensuring that the most appropriate healthcare treatment reaches the intended patient precisely when needed. This transformative influence of AI spans from the initial stages of drug discovery to enhancing the comprehension and utilization of clinical trial data, signifying the industry's consistent and progressive integration of artificial intelligence technologies.
Here Are Four AI & ML Trends in the Pharma Industry
Drug Discovery and Development using AI in Pharma
The time and cost to bring a new drug from concept to market is 7-10 years and costs $2 billion. source
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 Artificial Intelligence & 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.
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 databases source 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 in pharmaceutical industry and machine learning, pharma companies have shown more interest in developing drugs for rare diseases (source). 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.
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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 Pharmaceuticals (source), to grow and commercialize orphan drugs those greatly improved patient quality-of-life.
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, Deep (download PDF) 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’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 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 categorizes source 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 phase1 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.
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. The advancement of AI in Pharma industry has the capacity to dive into the data, identify patterns, and generate accurate hypotheses quickly making the process less expensive and more efficient and accurate.
In pharmaceuticals, AI has altered how medicines are formulated and brought to market. In the past, medicine formulation was a prolonged process that took years and incurred a substantial cost. However, now, thanks to AI advancements in healthcare, things have become much more efficient. AI has enabled a significant acceleration in medicine formulation. This is because technology companies and pharmaceutical companies are collaborating more than ever, resulting in exhilarating new products. The influence of AI also expands to all facets of the pharmaceutical industry. Plainly stated, AI in pharmaceuticals is akin to an extremely intelligent assistant that aids scientists and doctors in developing superior medicines more rapidly and in a more cost-effective manner. The expedited introduction of new treatments to patients enhances healthcare for all.
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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