The market for AI in the pharmaceutical industry is expected to increase from USD 303.50 million in 2019 to USD 3.88 billion in 2025, with a CAGR of 52.9%.
- Definition / Scope
- Market Overview
- Market Trends
- Industry Challenges
- Technology Trends
- Regulatory Trends
- Market Size and Forecast
- Market Outlook
- Distribution Chain Analysis
- Competitive Landscape
- Key Market Players
- Strategic Conclusion
Definition / Scope
AI is a broad area of computer science that makes machines replicate human intelligence with the system broadly called Machine Learning and Deep Learning. Other inter-related technologies include Supervised Learning, Unsupervised Learning, Natural Language Processing (NLP), Computer Vision, Speech and Robotics.
Machine Learning (ML) is an application of AI that provides systems with the ability to learn and improve from experiences and training, focusing on programs that can access data and use it to learn. Deep learning, a sub-set of ML, is a form of AI that mimics the neural networks of human body.
Machine is fed with terabytes of information then it studies them, learns from them, reiterates the process, and pivots its own moves. Supervised Learning uses labelled data to train a ML model while Unsupervised Learning is algorithm that take a set of data that contain only inputs and finds structure such as clustering. AI is first coined in 1956 by Dartmouth professor named John McCarthy.
Pharmaceutical involves drug discovery as a major crucial stage in which the extraction of ingredients from natural products or chemical compound occurs followed by finding potential treatment of a disease. The goal of drug discovery is to identify small molecules that selectively modulate functions of target proteins.
Historically, guidance has been provided by naturally occurring compounds that have been used medicinally for hundreds or thousands of years. As such, the natural world has provided a large number of molecules from which many modern drugs have been developed. Modern pharmaceutical development employs strategies and methods of evaluating a very large number of molecules for specific desired targets.
The global pharmaceutical market is estimated to reach USD 1.12 trillion in 2022 with a stable growth rate of 6.3% CAGR. Health care in the past followed a pattern whereby focusing on treating diseases once it appears rather than preventing it.
Recently, such flowchart has been transforming with the advent of AI and ML for value based-preventive measures. Ai-enabled solutions are emerging as a vital tool in transforming such process of researching disease mechanism of action and revolutionizing the understanding of how drug binds to targets, thus improving efficiency. AI applications in drug discovery have already delivered new candidate therapeutics, in some cases months rather than years.
Reducing time and increasing the accuracy are the two big advantage of AI in pharmaceuticals development. As of 2019, there are 190 AI companies, 50 corporations, 450 investors, and 40 major R & D centers.
North American market represents the largest market size during the forecast period 2019-2025, increasing from USD 128 million in 2019 to USD 1.52 billion in 2025, at a CAGR of 53.9%.
The European market captured 30.1% of the global market, from USD 90.89 million in 2019 to USD 1.14 billion, growing at a CAGR of 52.5%.
Likewise, Asian market grabbed 6.7% of worldwide share worth USD 105.2 million in 2019 and is projected to tally USD 1.05 billion by 2025, at a CAGR of 53.8%.
And the South American, Middle Eastern and African market combined make up 6.7% global share valued at USD 18.97 million in 2019 and will be worth USD 167.5 million increasing at a CAGR of 43.7%.
Furthermore, the US dominates the list of firms with highest VC funding in healthcare AI to date, and has the most completed AI-related healthcare research studies and trials.
But the fastest growth is emerging in Asia, especially China, where leading domestic conglomerates and tech players have consumer-focused healthcare AI offerings.
Europe, meanwhile, benefits from the vast troves of health data collected in national health systems and has significant strengths in terms of the number of research studies, established clusters of innovation and pan-European collaborations.
- A big chunk of available data
The data produced in health care is growing dramatically. For instance, the amount of genomics data generated in recent decades has increased from approximately ten megabytes per year in the mid-1980s to over 20 petabytes from 2015–19.
Electronic health records, medical imaging, insurance records, wearable and health applications, clinical trials are major sources of such data generation.
- Increasing health expenditure
Global expenditures on healthcare increased to 9.86% of total GDP in 2018, up from 8.6% in 2000. The US witnessed the highest expenditure on healthcare, 17.8% of total GDP, in 2015. The world’s population aged 60 years and above, is likely to grow by 56% from 2015 to 2030.
The shift towards an aging population will strain the current healthcare system. Because of these trends, the healthcare segment has a continuous shortage of nursing and technician staff.
The number of vacancies for nurses will be 1.2 million by 2020. AI is positioned to help medical practitioners efficiently achieve their tasks with minimal human intervention, a critical factor in meeting increasing patient demand.
- Growing number of investment and partnership
As of 2019, there are 190 AI companies, 50 corporations, 450 investors, and 40 major R & D centers. The cumulative number of companies partnering in pharmaceutical industry for offering technological leverage has staggeringly gone up from 4 in 2009 to 210 in 2020.
The highest number of increase happened during 2015-2018. Likewise, cumulative funding surged from USD 15.4 million in 2009 to USD 3.78 billion in 2019. The investment for a single year was recorded highest in 2018, around USD 1.16 billion. The cumulative number of collaborations rose from 1 in 2010 to 101 till 2019.
A large portion of the data collected by pharma companies is personal health data. Biopharma companies need to oblige about how information is collected and used so that individual privacy is protected and has become major constraints imposed by regulatory bodies and become limited to the extent of data that needs to be fed to AI but can’t access it because of the bar.
- Regulatory hurdle
AI based tools and machines need to feed vast pool of patient data coming from medical records, diagnostic images, or wearables. Using AI on a large scale will ultimately require large-scale data to power the algorithms and tools. Health data is a mix of public and private depository.
Accessing such data reliably and readily is the critical challenge. Inadequate data slows the development of AI technology since AI is a self-learning mechanism and all it needs is a large pool of data. State and federal regulators are a key hurdle facing AI and ML integration.
- Potential for increased unemployment
AI’s integration in the pharmaceutical industry creates fear of replacing humans in the workplace. Automation from AI-enabled bots will replace or augment work that humans have traditionally done, allowing employees to focus on higher-value activities while machines take on repetitive tasks that they can do faster, cheaper and more accurately.
- Skill gap
The skills gap is most acute for technical roles such as AI researchers, data scientists and software developers. In 2019, data science skills shortages were present in almost every large US city. There was a shortage of 151,717 people and growing faster, than the national shortage of software development skills.
AI allows computers to quickly identify small anomalies between an individual’s health data and that of similar patients or the wider population. Machine-learning algorithms can analyze CT scans in a fraction of the time that it takes the human eye.
|Drug Discovery||Data Mining, Neural Networking|
|Preclinical Research||Reinforcement Learning|
|Approval Process||Data Mining|
|Clinical Research||Image Recognition|
- 3D Printing
The 3D printing pill was first approved in 2015 by FDA. And this technology is now finding its way into prevention. It could help doctors locate and identify plaque in the arteries to help prevent heart attacks.
Norvartis in partnership with Proteus developed a sensory enabled smart pill that once swallowed can gather information that can be used to diagnose patients.
Verily Life Science in consolidation with Novartis developed smart contact lens that measures glucose levels in the wearer’s tears and can transmit data to a wireless device.
- Cellular Programming
In 2016, ViaCyte in consolidation with Janssen Technology started finding a Type 1 diabetes cure through stem cell treatment. Similarly, ISCO is developing a potential cure for Parkinson’s disease using stem cell therapy.
The Pharmaceutical Artificial Intelligence division of InsilicoMedicine is working on drug discovery programmes for cancer, Parkinson’s disease, Alzheimer’s disease and other age-related health issues.
- Predictive Analytics
The drug development and approval process it takes an average of 12 years for a drug to travel from the research lab to the patient. Only five in 5,000 (or 0.1%) of the drugs that begin pre-clinical testing ever make it to human testing and just one of these is ever approved for human usage.
Furthermore, on average, it will cost a company USD 359 million to develop a new drug from the research lab to the patient.
A key differentiator between an AI-enabled and a traditional drug discovery process is that AI replaces the need for a human to make a hypothesis where AI enables the use of patient-derived data to generate hypotheses.
Numerate uses advanced AI that can be applied to manage publicly and privately available health data to model absorb, distribute, and excrete properties in the drug development process.
- Machine learning – neural networks and deep learning
Machine learning is a statistical technique for fitting models to data and to ‘learn’ by training models with data. Machine learning is one of the most common forms of AI.
In healthcare, the most common application of traditional machine learning is predicting what treatment protocols are likely to succeed on a patient based on various patient attributes and the treatment context.
A more complex form of machine learning is the neural network which has been used for categorization of applications like determining whether a patient will acquire a particular disease.
The most complex forms of machine learning involve deep learning, or neural network models with many levels of features or variables that predict outcomes. A common application of deep learning in healthcare is recognition of potentially cancerous lesions in radiology images.
Deep learning is also increasingly used for speech recognition and, as such, is a form of natural language processing (NLP).
- Natural language processing
NLP includes applications such as speech recognition, text analysis, translation and other goals related to language. There are two basic approaches to it: statistical and semantic NLP.
Statistical NLP is based on machine learning (deep learning neural networks in particular) In healthcare, the dominant applications of NLP involve the creation, understanding and classification of clinical documentation and published research.
Pharmaceutical companies need to comply with a variety of data protection requirements including consumer protection and Health Insurance Portability and Accountability Act (HIPAA) legislation as well as international laws such as Europe’s General Data Protection Regulation (GDPR).
HIPAA addresses the use and disclosure of individual’s health information- called protected health information. Its major goal is to assure that individuals’ health information is properly protected while allowing the flow of health information needed to needed to provide superior health care service.
GDPR is aimed at standardizing and strengthening the protection of personal data, including strengthening the rights of individuals to be better informed about how their data are to be used, it also sets out clear responsibilities and obligations on health care professionals and companies using such data, with stringent penalties for infringements
Currently, regulators require manufacturers of AI algorithms to resubmit clearance applications when major modifications are made to software, as regulators are ill-equipped to work with algorithms that constantly change through self-learning.
However, in 2019, the FDA allowed companies to include plans for any anticipated modifications to their algorithms with feedback required. Similarly, the European Medicines Agency (EMA) has recommended the establishment of an AI test ‘laboratory’ to explore how AI and other digital technologies can be exploited in decision-making across key business processes.
Pharma companies should therefore think about engaging proactively with the FDA, EMA and other regulators to ensure future guidance from regulators is fit for purpose.
There are also other ethical considerations on which AI companies need to lay the technological advancement. Technology companies and tech giants are creating AI tools and platforms and have developed ethical guidelines to govern the use of AI internally as well as guide other enterprises.
Governments and regulators are also beginning to play a crucial role in establishing policies and guidelines to tackle AI-related ethical issues. For example, the European Union’s GDPR requires companies to be able to explain decisions made by their algorithms and allow individuals to request their data be anonymised, deleted or forgotten.
Other government initiatives include setting up AI ethics councils or task forces, and collaborating with other national governments, industry and other stakeholders. Consequently, pharma companies need to build in ethical considerations as they design, build and deploy AI-powered systems, including testing for and remediating systems that unintentionally encode bias and treat users or other affected parties unfairly.
Market Size and Forecast
The market for AI in the pharmaceutical industry is expected to increase from USD 303.50 million in 2019 to USD 3.88 billion in 2025, with a CAGR of 52.9%.
Drug discovery represents the largest market size during the forecast period, increasing from USD 242.91 million in 2019 to USD 2,999.7 million in 2025, at a CAGR of 52.0%.
The market for precision medicine is estimated to grow with the highest CAGR over the forecast period of 60.2%, from USD 26.11 million in 2019 to USD 440.9 million. Medical imaging & diagnostics and research are also expected to grow at CAGR over 50%.
Growing at a CAGR of 52.9%, the global market for AI’s application in pharmaceutical is expected to be worth USD 464.05 million by the end of 2020. With the same growth rate, the market size will hit more than double in 2022 and surpass more than five-fold in 2024, worth USD 2.5 billion by 2024.
Distribution Chain Analysis
Drug discovery generally comprises five main stages as depicted in the figure below. These stages take 5-6 years on average depending on the complexity of the disease and the ingredients as a candidate excluding drug target identification stage.
The average time to bring a molecular ingredient through to launch is 10-12 years. The average R & D cost is estimated around USD 2.168 billion per drug.
In the Research & Discovery phase, the application of AI could be synthesising information, data mining to identify candidate and molecule interactions, understanding disease mechanism and drug candidate selection.
Clinical development phase could be occupied with AI to trail the design, to select site, to predict risk, to monitor drug adherence. It will make research and discovery cheaper, faster and more effective. It will also reduce the number of failed drugs by identifying patterns in large volumes of data.
Till date, there are 148 start-up companies and 33 large companies are applying AI in research & discovery of a new drug worldwide. During the manufacturing & supply stage, all the planning, maintenance, management, forecasting, procurement of inventory tasks could be efficiently managed with the use of AI.
Similarly, launching, patient engagement with the new drug, marketing strategies during the launch phase and medication adherence, patient monitoring, support programs during the post market surveillance phase are enhanced with better decision by the application of data driven methods.
The AI pharmaceutical market is highly fragmented and characterized by three major categories of companies:
- Diversified healthcare corporations increasingly developing AI capabilities
- Technology giants exploring AI applications in multiple industries and
- AI-focused startups (See Figure).
Most of the pharmaceutical companies are leveraging with AI-driven companies for drug discovery and the most preferred strategy they see is through strategic alliances.
Since these partnerships are in the areas, which are core to a pharmaceutical company, some pharmaceutical companies are also of the view that they need to develop these capabilities internally.
|Pharma Companies||AI-Driven Companies|
|Johnson & Johnson||GNS Healthcare|
Key Market Players
Atomwise is a drug developing platform founded in 2012 designed to analyze new medicines using AI and neural networks. The company’s platform helps in drug hit discovery, binding affinity prediction and toxicity detection, enabling scientists to discover small molecules for the treatment and investigation of human diseases.
It raised the staggering Series A fund of USD 45 million in 2018 tallying the total capital raise of USD 51 million. The company claims that it currently screens more than 10 million compounds each day.
AirCure is an operator of an AI and advanced data analytics with its proprietary intelligent software that captures and understands video, audio, and behavioural data to establish the link between patients, disease and treatment.
When a nurse cares for a patient, he or she uses visual cues to assess, interact, and assist patients, ensuring high standards of care. AiCure is automating this form of visual observation and interaction through intelligent software.
The core platform offers interactive assistance to patients on their smartphone, performs visual dose confirmation, allows for coordination of care, and is even starting to visually assess disease progression over time.
In 2015, it acquired USD 15 million in Series B funding. With another round of funding tallying USD 12.25 million in 2016 and USD 24.5 million in Series C funding in 2019, the total investment that it has acquired so far reaches USD 51.75 million.
Numerate is a privately held biotechnology company found in 2007 that focuses on the application of Artificial Intelligence and machine-learning algorithms to discover molecule drug.
Numerate’s drug design platform combines advances in computer science and statistics with traditional medicinal chemistry approaches to develop a pipeline of drug programs in the cardiovascular, metabolic and neurodegenerative disease areas, focused on targets not typically addressed by computer-aided drug discovery.
In 2010, FDA first granted patent protection for methods of using biological essay data to develop predictive models to Numerate. The company has first raised USD 8 million in Series A round funding in 2014. After Series B and Series C round funding, the total investment reached USD 19.5 million.
Exscientia Ltd (Exscientia) is an AI driven start-up established in 2012 that automates drug discovery and design process leveraging artificial intelligence and big data technologies.
It has collaborated with top pharma companies like Roche, Sanofi, Bayer, Celgene, and GlaxoSmithKline in Europe. In 2020, the company raised USD 60 million in Series C funding from Novo Holdings and Bristol Myers among others. Previously, it had raised combined investment of USD 42.66 million through Series A and Series B funding.
- GNS Healthcare
GNS healthcare applies its patented Reverse Engineering Forward Simulation (REFS) machine learning platform for predictive analysis in drug discovery. Recently in 2019, it bagged USD 23 million in Series D investment backed by Cigna and Celenge.
It uses streams of patient data, including data from electronic medical records, mobile health devices, medical and pharmacy claims, genomics, consumer behaviour and processes these data in the cloud platform to better match drug to patients.
There has not been approval for any AI-derived drugs from regulators yet, nor have any validated in clinical trials yet. Nevertheless, the first such mile stone is expected to be reached at the end of 2020 and the adoption of AI is enabling more precise target treatment and shifting the pharma value chain towards more predictive and effective drug development.
The fact can be backed with the data that more than 20% drug approval rate was witnessed in 2020 than in 2018 and 2017. Currently, AI’s adoption has been limited to analyzing big data in healthcare, however, in the future, the AI will help train big data to design new precise drug candidate.
- AI- Artificial Intelligence
- Pharma- Pharmaceutical
- NLP- Natural Language Processing
- ML- Machine Learning
- CAGR- Cumulative Annual Growth Rate
- R & D- Research and Development