The impact of artificial intelligence on the pharmaceutical sector

The matrix below details the areas in AI where pharma companies should focus their time and resources. We suggest they invest in technologies shaded in green, explore the prospect of investing in technologies shaded in yellow, and ignore areas shaded in red.

Although AI is beneficial for many areas across the pharma industry, companies should focus on investing in AI for drug discovery, accelerating processes, and enhancing efficiency in manufacturing and supply chains. Advancements in AI have many possibilities for increasing patient quality of care and health outcomes. These include increasing the number and time of new drugs on the market, faster and earlier diagnosis, access to improved clinical trials, and the potential for personalised medicine.

In drug discovery, AI can analyse vast amounts of data to detect patterns and trends and assess the biological performances of drug candidates to find the best for clinical testing. Generative AI, for example, can predict novel drug candidates or molecular structures based on patterns and trends found in data.

For example, biotechnology company Insilico Medicine announced in April 2023 the successful discovery of an inhibitory of CDK8 for the treatment of cancer using its generative AI-driven learning platform Chemistry42.

Generative AI can also be used to design novel chemical compounds. For example, researchers at the University of Toronto have developed an AI system that can create proteins not found in nature (Lee et al., 2023). The system uses the same technology as OpenAI's Dall-E image creation platform. In the future, this could assist in discovering new drug candidates with unique properties, with the potential to create treatments for diseases that currently have no effective options.

AI can also accelerate and digitalise clinical trial processes to complete studies faster. AI can optimise the design of patient-centric clinical trials and allows technologies such as wearables, heart monitors, and body sensors to collect patient data remotely and less invasively. Remote monitoring and decentralised clinical trials provide an attractive way to increase patient participation and access a more diverse group of participants.

Pharma companies should invest in AI tools for efficiency in manufacturing, distribution, delivery, and streamlined supply chains. Through AI, many areas of a medical supply chain can be automated, including inventory tracking, managing, and restocking processes. Investing in AI-powered tools such as robotics and drones will also speed manufacturing processes by assisting in supply delivery.  

Generative AI may also assist in improving communication and streamlining in manufacturing. ChatGPT could create optimisation plans, predictive maintenance schedules, and detect anomalies in available information.  

Between April 5 and April 25, 2023, visitors to GlobalData’s Pharmaceutical Technology and Clinical Trials Arena websites were asked which area of the pharma industry would benefit the most from generative AI. Most respondents selected drug development and discovery, followed by clinical trials, and sales and marketing.

Over the past several decades, advances in computational technology have allowed increased exploration of the vast chemical space. Computer-aided design and drafting, or in silico approaches (experimentation performed by a computer), are widely used to enhance traditional drug discovery methods and can reduce the time and cost of drug development, with significantly higher hit rates.

However, success rates are still low, with just 10% of candidates making it through preclinical development and into clinical trials. The low success rates and the resulting financial cost has led to the need for alternative approaches. AI has shown enormous potential to further enhance in silico methods by rapidly ingesting and exploring the expanding chemical space, driven by the ever-growing amount of big biomedical data.

Training AI on the vast amounts of genomic data, health records, medical imaging, and other patient information has allowed an increased understanding of the biological mechanisms of diseases. AI-assisted mapping of disease pathways and protein and drug interactions through specialist vendors has created a deeper understanding of targets and assisted in identifying novel proteins and genes that can be targeted to counteract them.

AI can also be used to predict three-dimensional (3D) structures of targets and accelerate the design of appropriate drugs that bind to them, through protein structure databases such as DeepMind’s AlphaFold.

Once a set of promising lead drug compounds have been identified, AI can then assist in candidate drug prioritisation, ranking molecules for further assessment. Aside from identifying drug targets, AI has also been used successfully in the virtual screening of compounds, such as identifying those that can bind to ‘undruggable’ targets, de novo drug design, drug repurposing, and identification of treatment response biomarkers (Patel et al., 2020; Chong et al., 2021).

AI algorithms have shown the potential to condense a typical four or five-year exploratory research phase into less than a year, significantly reducing the time and cost to get a drug to market, which is particularly important for indications with few treatment options.

There have been some significant milestones in the use of AI in drug discovery in 2023. In January 2023, AbSci became the first company to create and validate de novo antibodies in silico using zero-shot generative AI. Creating de novo therapeutic antibodies in silico could reduce the time it takes to get new drug leads into clinical trials by more than half. The zero-shot generative AI model generates novel antibody designs that are not found in any existing databases.

In February 2023, Insilico Medicine was granted orphan drug designation by the FDA for INS018_055, a small molecule inhibitor treatment for idiopathic pulmonary fibrosis (IPF). INS018_055 was discovered and designed through the company's generative AI platform, Pharma.AI. The drug will enter a global, randomised, multi-site Phase II trial in 2023.

The total time from discovery initiation to the start of Phase I took under 30 months for INS018_055, representing a significant increase in speed for therapeutic asset development in the pharma industry. Despite these advancements, most novel drugs developed using AI are in the preclinical or discovery stages, and it could be many years before an AIbased therapy is approved.

While AI has shown the potential to transform drug discovery processes, it faces many challenges. These include the quality and appropriateness of data, continued assurance of drug safety and efficacy, educating the scientific community to increase buy-in, and issues around intellectual property rights.

Although the impact of AI on traditional drug discovery is in its early stages, we have already seen that when layered into a traditional process, AI-enabled capabilities can substantially speed up and reduce the costs of running expensive experiments, which has the potential to be transformative for patients, medical providers, and the pharma sector.

Clinical trials are essential to ensure the safety and efficacy of all potential drugs before they enter the market. However, this process remains slow, expensive, and unpredictable. AI tools can potentially improve the success and efficiency of clinical research. 

Recruitment of patients for clinical trials remains a critical challenge, especially the enrolment of diverse populations. Around 80% of global clinical trials fail to recruit and retain enough patients to enrol on time (Desai, 2020). The impact of poor recruitment on a trial is immense in terms of time and associated financial burdens. AI can identify potential candidates for clinical trials by analysing vast amounts of patient data and medical records. It can also analyse social media content to identify specific regions where a condition is more prevalent, narrowing the search for an optimal cohort.

Once the desired patient subgroups are detected, AI can also assist and accelerate trial recruitment. Traditionally, eligible patients are found though hospitals or clinics, but often when recruiting large numbers of people, only a few are eligible. AI technologies can alert medical staff and patients to trial opportunities and reduce unnecessary screening of patients. It can also help to simplify entry criteria to be more accessible for potential participants.

AI-based analysis can also provide insight into participant behaviour, and researchers can use this information to design more efficient and effective clinical trials. Furthermore, AI can analyse patients’ genetic makeup, other physiological data, and lifestyle factors, helping to identify specific patient populations likely to benefit from a particular drug. A personalised treatment plan for a potential high-risk cancer patient could involve earlier screening, which may help with earlier diagnosis and treatment and lead to improved quality of life for the patient.

The average dropout rate for traditional clinical trials is about 30%, depending on the trial phase (CenterWatch, 2016). Patients may discontinue trial participation for various reasons, including inconvenience, the number of visits required, complex trial protocols, lack of support, and little or no reimbursement of expenses.

Through AI, health assessments can be performed remotely in patients’ homes, improving patient experience and ease during clinical trials and boosting patient participation and retention. Decentralised clinical trials reduce reliance on in-person trial sites and overcome any barriers related to inaccessibility, especially for underrepresented populations. Patients' vital signs and other information can be collected remotely through wearable devices and mobile apps.

Real-time tracking of medication can also help reduce medical non-adherence. The traditional process of manually tracking medication, through reliance on a patient’s memory, is prone to error and can limit clinical trial outcomes. One example is the clinical trial management AI-platform AiCure, an interactive medical assistant that helps to spot patients at risk of non-adherence.

Patients on a clinical trial are signed up to the platform via mobile app, where they record their progress and receive reminders to take medication. Patients record a video of themselves taking their clinical trial medication as proof, and the AI-platform can then identify the correct patient and pill, to ensure the patient is engaging with the clinical trial properly.

Each clinical trial generates vast amounts of data that researchers must manually review to uncover meaningful insights. AI can analyse this data efficiently, finding patterns that may be missed by human analysis.

For example, AI-based models can predict the toxicity of a potential drug candidate, helping researchers select suitable compounds that can then be used in a trial. Organisations can also use AI technologies to find suitable existing data for new trials, reducing the need to start from scratch across trials and speeding up the design process.

While the application of AI shows promises to increase the efficiency of clinical trials, universal data management and inherent bias in data need to be addressed to use AI successfully. The differences in regulation between countries limit the scope for collaboration and cooperative research and may restrict the ability to train AI systems on a global scale. AIbased systems also raise data security and privacy concerns. Therefore, maintaining the confidentiality of medical records used in clinical trials is crucial.