Prescribing AI for the Pharmaceutical Industry
Pharmaceutical companies are at the forefront of developing life changing medicines. From research in the lab to post market surveillance, pharmaceutical companies are increasingly utilising technology to transform their operations and increase efficiency when bringing drugs to market. In today’s climate, pharmaceutical companies must take advantage of emerging technologies across their value chain to improve performance.Using AI to optimise their processes will allow companies to tackle the problem of asset lifecycle compression- the decreasing amount of time available for companies to capture a drug’s value due to the time period between launch and increasing competitive pressures. Faster, more efficient processes will hopefully allow drugs to reach the market sooner, benefiting stakeholders, whilst also giving biopharma companies more time to generate value.
At the beginning of the drug development process, the pharma industry accesses real-world data from a variety of sources, ranging from insurance records and clinical trials to health apps. With this large data pool, AI can be used to integrate and analyse real-world data from different sources. In particular, machine learning can process datasets and natural language processing can analyse scientific and medical literature and documents. The next step in drug discovery involves scientists extracting knowledge about diseases, drug targets and previous clinical trials. This process can be time-consuming, but also prone to inaccuracies due to the vast amount of information that scientists must sift through. Therefore, GPT-powered knowledge extraction can be employed. This AI can understand scientific and medical publications, extract relevant information, significantly reducing time spent by researchers, who could better focus on higher value scientific analysis.
After researchers have identified a viable drug candidate, the next stage is recruiting participants for clinical trials. However, identifying patients can be difficult due to the specific eligibility criteria required for a trial. AI can address this by analysing genetic, phenotypic and real-world data to identify biomarkers associated with disease progression or response to treatment. Patients can then be stratified according to their predicted response to treatment, helping researchers identify eligible participants and create more diverse and representative trial populations, which can contribute to more efficient trials. AI can also be used during clinical trials. Generative AI can analyse data such as the number of patients recruited, patient dropout rates and delays to identify barriers to enrolment and suggest interventions. It can also analyse site performance and clinical data, working alongside clinical managers to find inefficiencies and suggest interventions to accelerate the process. Conversational AI can also be used to support cohesion between cross-functional teams.
AI can also be utilised in supply chain and production. Its predictive capabilities can anticipate future supply issues and bottlenecks through its analyses of historical and market trends. The technology can generate plans for proactive intervention to reduce production inefficiencies.
In the pharmaceutical industry, marketing is pivotal for successful commercialisation of drugs and for creating value for stakeholders. Artificial intelligence can support this process by allowing companies to model scenarios and assess outcomes. Eularis allows biopharma companies to test out how their decisions, as well as their competitors’ actions, will affect the market. The technology can allow pharma companies to prepare for a product launch by analysing clinical trial data, market landscape and concerns. It can also predict which physicians are most likely to switch to a new drug, allowing companies to focus their resources on stakeholders most likely to engage with their product. An example of a company utilising AI’s predictive abilities in Pfizer Australia, which partnered with Complexica on its What-if Simulator and Optimiser. It can model a variety of scenarios to simulate impacts of sales and marketing strategies. Many companies are also exploring AI-enabled omnichannel marketing. This allows companies to analyse different data sources like health records, online platforms and demographic, geographic, socioeconomic and historical sales data to provide recommendations on how to best improve stakeholder engagement and increase product visibility. AI can also be used post-launch to monitor audiences’ responses to marketing and promotional activity. Synthesio has a ‘Social Intelligence Suite’ which detects conversational patterns, trending images and buzzwords. This data can then provide marketing teams with insights allowing them to identify the appropriate promotion opportunities for their products and clients.
Whilst AI has vast potential to transform the biopharma field, there are many challenges associated with AI adoption. One concern is data privacy and security- when handling confidential health records, it is imperative that there are regulations in place to ensure patient confidentiality and privacy are maintained. There are also many logistical problems to consider. One being data interoperability, where companies must ensure that data from different systems can be exchanged and understood consistently. Additionally, for companies to adopt AI across all areas of their business, both their systems and staff must be prepared. It is important for companies to have the appropriate software and technical infrastructure to support implementation of AI, whilst it is also equally important for companies to invest in their employees to ensure that they are AI-competent and know how to use AI effectively alongside their existing work, to allow for a seamless transition to AI-enabled processes.
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