What do we mean when we talk about AI within pharma?
Pharmaceutical AI (Artificial Intelligence) technology refers to the application of AI techniques and algorithms in the field of pharmaceutical research, drug development and healthcare. It involves the use of machine learning, deep learning, natural language processing (NLP), and other AI methods to manage and analyse large volumes of data, generate insights, and make predictions in the pharmaceutical domain.
Artificial Intelligence (AI): AI and machine learning are being increasingly applied in pharmaceutical research and development processes. These technologies can help identify patterns and accelerate drug discovery by predicting the effectiveness of drug candidates and optimising clinical trial design.
Which Pharma companies are using AI?
It is fair to say that in recent years, AI has been revolutionising the pharmaceutical and drug development industry across a number of product areas. Industry giants such as GSK, Pfizer and Merck & Co. have already collaborated with companies developing AI technologies and more will follow. In fact, according to industry estimates, the Pharma industry’s spending on AI is expected to reach $3 billion by 2025.
Janssen is embracing AI’s potential in various drug development domains, ranging from target discovery to clinical trials and is exploring the use of AI across it drug development activities. AI is being utilised across areas such as drug discovery, clinical trial design, patient identification and manufacturing optimisation. To date (2023), the drug company has more than 100 ongoing AI projects.
Lonza is participating in the digitalisation of manufacturing through data, machine learning, and artificial intelligence applications.
AstraZeneca has collaborated with more than 100 prominent Pharma partners and AI providers since 2015. These schemes involve applying machine learning to the identification and development of novel therapeutics for chronic renal disease and idiopathic pulmonary fibrosis.
AstraZeneca is also using AI and data science across its R&D projects, partnering with Viking Therapeutics, to speed up the development of NASH drugs and the sequencing behemoth Illumina to identify new drug discovery targets.
How is AI being utilised in the pharmaceutical sector?
Key areas where AI is being utilised across the pharmaceutical sector are as follows:
- Drug Discovery and Development: AI is being employed to accelerate the drug discovery process by predicting the properties of molecules and identifying potential drug candidates. Machine learning algorithms can analyse vast amounts of chemical and biological data to identify patterns and relationships, leading to the discovery of novel drug targets and compounds. This helps in reducing the time and cost involved in the initial stages of drug development.
- Target Identification: AI algorithms can analyse biological data, such as genomics and proteomics, to identify potential drug targets. By mining large-scale datasets and performing complex analyses, AI can prioritise targets that are most likely to be effective for specific diseases, allowing researchers to focus their efforts more efficiently.
- Predictive Analytics: AI can help predict the success or failure of drug candidates by analysing various factors such as chemical structure, biological activity, and clinical trial data. Machine learning models can be trained on historical data to identify patterns and make predictions about the safety and efficacy of new drugs, enabling researchers to make more informed decisions.
- Clinical Trial Optimisation: AI can optimise the design and execution of clinical trials by identifying patient populations that are most likely to benefit from a specific drug. By analysing patient characteristics, genetic profiles, and historical trial data, AI algorithms can help identify the right patient cohorts, resulting in more efficient and successful clinical trials.
- Drug Repurposing: AI algorithms can analyse existing drugs and their molecular structures to identify new therapeutic uses. This approach, known as drug repurposing or drug repositioning, can significantly reduce the time and cost of developing new drugs by leveraging existing compounds that have already undergone safety testing.
- Personalised Medicine: AI can help tailor treatments to individual patients by analysing patient data, including genomic information, medical history, and lifestyle factors. By considering this personalised data, AI algorithms can assist in predicting treatment responses, optimising dosage, and identifying potential adverse reactions, leading to more effective and safer treatments.
- Image Analysis and Diagnosis: AI can analyse medical images, such as CT scans, MRIs, and pathology slides, to assist in the diagnosis and treatment of diseases. Deep learning algorithms can detect patterns and abnormalities in medical images, providing more accurate and efficient diagnoses, and supporting physicians in decision-making processes.
However, it's important to note that while AI has immense potential in the pharmaceutical and drug development industry, it is still a complementary tool to human expertise. Human oversight, validation, and regulatory compliance remain critical to ensure patient safety and ethical standards are upheld.
What other emerging technologies are we seeing in Pharma?
Personalised Medicine: There is a growing focus on personalised medicine, which involves tailoring treatments to individual patients based on their genetic makeup, lifestyle, and other factors. Advances in genomics and molecular biology have enabled researchers to better understand the genetic basis of diseases, leading to the development of targeted therapies.
Gene Editing: Technologies like CRISPR-Cas9 have revolutionised gene editing and have the potential to transform the treatment of genetic disorders. Gene editing allows for precise modifications to the DNA sequence, opening up possibilities for correcting disease-causing mutations.
Immunotherapy: Immunotherapy has emerged as a promising approach for treating various types of cancer. It involves using the body's immune system to target and destroy cancer cells. Techniques like immune checkpoint inhibitors and chimeric antigen receptor (CAR) T-cell therapy have shown remarkable results in certain cancers.
Digital Health: The integration of digital technology in healthcare is transforming the pharmaceutical industry. Mobile health apps, wearable devices, and remote monitoring tools are being used to improve patient care, medication adherence, and clinical trial management.
Continuous Manufacturing: Traditional batch manufacturing processes are being replaced by continuous manufacturing in some pharmaceutical companies. This approach allows for more efficient production, reduced costs, and better quality control.
Nanotechnology: Nanotechnology offers new possibilities in drug delivery and formulation. Nanoparticles can be engineered to enhance drug stability, improve bioavailability, and target specific sites within the body.
3D Printing: Additive manufacturing techniques, such as 3D printing, are being explored in pharmaceuticals. This technology enables the creation of personalized dosage forms with complex geometries and controlled release profiles.
What is the future of tech and AI in the Pharma industry?
While technology plays a vital role in the pharmaceutical industry, its application and focus areas vary across sectors. Each sector leverages technology to address industry-specific challenges, optimise processes and deliver value to their respective stakeholders. The future of AI in the pharmaceutical industry is expected to be transformative, with the potential to revolutionise various aspects of drug discovery, development, and healthcare delivery.
With a current value of nearly 100 billion U.S. dollars, investments are expected to grow by 2000% by 2030, up to nearly two trillion U.S. dollars. Nearly 50% of global healthcare companies will implement artificial intelligence strategies and by 2025 and some experts believe it is crucial for how businesses operate down the line. AI and machine learning will continue to help further drug discovery and manufacturing and as AI tools become more accessible over the years, they will become part of the natural process within pharmaceutical and manufacturing. The future of Pharma will be AI-enabled.
We hope this article has been helpful. More information can be found in our references below.
Looking for a new role?
You can check out all the current vacancies we have available via our pharma jobs page
Are you looking for new talent within Life Sciences sector?
Carrot Recruitment have over 17 years experience, recruiting across the full Pharma product life cycle. We support clients across all functions, from product development to commercialisation.
We have a deep reach into many niche candidate networks with over 20,000 candidates on our books. Candidates love our approach and work with us throughout their career, which means we have a huge catalogue of top talent at our fingertips.