Enabling drug design and development:
Vast amounts of structured and unstructured biomedical data, available from clinical trial publications, journals, EMRs, social media, and laboratories, hold key insights for researchers to implement in future drug research. AI offers an effective way to collate and analyze this data through scalable algorithms that study varied types of information, including gene expression, sequencing, molecular, protein, and drug interaction data, in order to determine potential drug structures, discover obscure patterns in drug usage, and examine side effects. ML and AI further offer important applications in drastically reducing R&D cycles by years as well as costs per patient, in addition to enabling predictive modeling for the development of medication at reduced turnaround times.
Fine-tuning clinical trials:
On average, a timeframe of 10–15 years and USD 1.5–2 billion are required to bring a new drug to the market. The use of automated data analysis pipelines can significantly reduce the time and costs involved in integrating information from varied data streams and deriving actionable insights. Such pipelines can effectively enable the development of AI-powered dashboards, which in turn can determine the safety of drugs administered, trial performance, intervention success rates, patient symptoms and dosing, and the potential for adverse reactions. The availability of real-time data through these dashboards can further map behavioral trends to comprehensively gauge a drug’s impact, with proactive, informed decision-making made possible through clear-cut and easily understandable data. Further, AI-powered dashboards can provide crucial insights into subject, region, and study-specific as well as portfolio-level progress.
Targeting patients and studying behavior:
Identifying and following up with patients for clinical trials can prove time-consuming and expensive. Data-driven searches leveraging health, demographic, location, and genetic information can rapidly identify appropriate populations for trials, especially patients belonging to sub-phenotypes, facilitating effective cohort composition and patient recruitment and monitoring. Moreover, the use of AI-linked wearables can provide granular and continuous data on participants’ health statuses, behavior, and non-adherence, facilitating remote monitoring and follow up as well as reliable endpoint detection and assessment, patient retention, and adherence control.
Bolstering Pharmacovigilance and Risk Management:
Pharmacovigilance requires focused and actionable insights into drug safety, quality, and compliance processes. At present, traditional signal detection and examination methods, including studying various reports, databases, and clinical trials, continue to be used by most companies in the industry; here, AI can help extract and derive data from other valuable and digitalized sources, including the internet and real-world evidence (RWE) to examine patient characteristics and obtain information on drug performance and differentiation. This is crucial to understanding public sentiment, detecting early problems with drug use amongst populations, and charting the impacts and safety of drugs for patients. Analytics can also provide deep insights into and create learning systems for improved drug risk-benefit profiles, further enabling effective reporting for regulatory processes and decision-making for treatment options.
Optimizing HCP outreach:
Pharmaceutical companies are increasingly eschewing traditional outreach strategies in favor of data-driven and hyper-targeted campaigns that use predictive analytics and ML capabilities to identify potential customers and HCPs through healthcare datasets, interactions with pharma reps, and even social media. HCPs who are open to prescribing new treatment options can be identified based on data available regarding networks, prescribing behavior, and prescribing value to ensure personalized, convenient, and consistent messaging across platforms that caters to their individual prescribing patterns.