Growing AI Applications in Biopharma and Biotech
As technology advances, Artificial Intelligence (AI) is becoming an increasingly useful tool in the pharma and biotech industries. The size and speed at which AI can analyze data sets is helping companies more efficiently discover, manufacture, and commercialize drugs. Additionally, AI is being used to improve the understanding of disease pathophysiology, further advancing drug development. PharmaNewsIntelligence reported that “nearly 62% of healthcare organizations are thinking of investing in AI in the near future, and 72% of companies believe AI will be crucial to how they do business in the future”.
Discovery/Development
Drug Discovery
AI algorithms can predict drug interactions, optimize chemical structures, and identify new drug candidates by analyzing vast amounts of chemical and biological data. AI can also identify new therapeutic uses for existing drugs by analyzing data on drug mechanisms, biological targets, and clinical outcomes.
Atomwise is an AI-driven company using deep learning algorithms to predict small molecule bioactivity. BenevolentAI has developed an AI platform that can help understand complex disease biology, make data-driven decisions, and select the right drug target from the outset. The flexibility of this model allowed them to pivot the focus from new drug development to identifying new therapeutic uses. Within 48 hours, they discovered a potential treatment for COVID-19 called baricitinib, which was initially developed to treat rheumatoid arthritis.
Precision Medicine
AI can help analyze patients' genetic, clinical, and environmental data to provide personalized treatment plans, considering individual variability in genes, environment, and lifestyle. AI algorithms can analyze large datasets, such as genomic data, proteomics, and metabolomics, to identify potential biomarkers for disease diagnosis, prognosis, and treatment response, allowing for more targeted treatments to be developed.
Zephyr Health, an AI-driven company, utilizes machine learning algorithms to mine and analyze large datasets to identify potential biomarkers for diagnosing and treating various diseases.
Clinical Trials
AI tools can reduce the number of trial participants and length by optimizing clinical trial design. AI has significant implications in recruitment, where it can perform automated eligibility analysis, match potential participants to trials, and simplify trial searching capabilities. These tools can also facilitate more comprehensive statistical analysis.
Deep 6 AI is an AI-based platform that accelerates patient recruitment for clinical trials by using natural language processing (NLP) to analyze patient records and match them with the trial criteria.
Manufacturing/Supply Chain Management
Drug Manufacturing
AI can optimize the drug manufacturing process in process design and control, smart monitoring and maintenance, and trend monitoring for continuous improvement. Leveraging process development data, optimal parameters, or scaled-up processes can be identified to reduce development time and waste. Real-time monitoring of equipment and production can improve quality and reduce waste, while trend observation can observe root cause identifications.
Aizon is a company that uses AI technology to optimize biopharmaceutical manufacturing processes, monitoring production parameters in real time and adjusting them to maintain product quality and reduce waste.
Supply Chain Management
AI can help optimize supply chain operations by forecasting demand, managing inventory, and identifying potential bottlenecks. This can eliminate inefficiencies in the supply chain, increasing operation accuracy, repeatability, and throughput.
TraceLink is a company that offers an AI-driven platform for managing the pharmaceutical supply chain. It delivers track- and -trace solutions to meet regulatory requirements applications, helps companies predict demand, optimize inventory levels, and manage recalls.
Regulatory Compliance and Pharmacovigilance
AI can improve post-market drug safety monitoring by analyzing large volumes of adverse event reports, identifying trends and signals, predicting potential safety issues, and assisting with regulatory compliance. AI can improve the speed of adverse event detection and risk assessments, the accuracy of dosing recommendations, and the prioritization of clinical trials.
VigiLanz Corporation has developed an AI-driven platform called VigiLanz Dynamic Safety Surveillance, which helps healthcare providers monitor adverse drug reactions, identify potential safety concerns, and comply with regulatory requirements.
Commercialization
Achieving Marketing Excellence
AI algorithms can analyze historical sales data, market trends, and competitor information to forecast demand and optimize pricing strategies. Analyzing customer data and using AI tools to monitor social media channels and online forums to gauge public sentiment can improve the personalization and effectiveness of marketing campaigns.
Engaging Customers
AI-powered virtual assistants can provide information to patients, healthcare providers, and researchers, answering questions about drug interactions, side effects, and other relevant information. Such platforms can also help patients adhere to their medication regimens by providing reminders, personalized health information, and support. Finally, AI can enhance customer relationship management systems by analyzing customer data, identifying patterns, and predicting customer behavior. This can help pharma companies improve customer engagement and retention.
Sensely has developed an AI-powered virtual assistant called "Molly," which helps patients and healthcare providers access medical information and advice on drug interactions, side effects, and more.