New technologies are supercharging drug development. AI innovations enable pharmaceutical companies to bring safer, more effective medications to market faster. This new age of medicine creates exciting new possibilities for patients and drug manufacturers, but it also brings new challenges.
The recent surge of highly effective weight loss drugs demonstrates the revenue opportunities and complexities facing drug manufacturers. Drugs such as Ozempic and Wegovy deliver unprecedented benefits, skyrocketing demand and straining supply chains. AI may make high-demand drugs more common, so pharma manufacturers must invest in data and analytics in order to optimize revenue.
The promise of AI in drug discovery
Drug development typically takes more than a decade, but AI and machine learning can greatly shorten that timeline. Experts estimate the market for AI in drug discovery will increase from $1.6 billion to $5.7 billion by 2028.
Al solutions can collect and analyze massive datasets to rapidly screen millions of potential drug molecule combinations for effectiveness and identify the most promising candidates. Optimizing this preclinical phase reduces risk and shortens timelines by 40%. In one real-world example, AI cut the time to select a preclinical candidate from 3-6 years to 18 months, saving about $1.8 million.
Genomics and genetic sequencing innovations will help identify novel disease targets and biomarkers for more precise therapies tailored to specific diseases and patients. Advancing nanotechnology likewise gives drug developers new options for customized drug delivery mechanisms.
AI-powered tools can identify ideal clinical trial candidates, analyze trial outcomes more quickly, uncover subtle trends and patterns, and monitor and predict adverse events. These capabilities improve patient safety and data quality.
Outside of advanced data analysis, AI technology can also eliminate tedious administrative tasks and streamline workflows for more efficient and effective processes.
What accelerated drug development means for pharma manufacturers
Pharma companies have an opportunity to improve millions of lives with these new drug development tools. But, manufacturers must be prepared to meet demand and execute effective price management strategies.
Demand forecasting is especially challenging for new and groundbreaking drugs. Pharma companies must leverage all available data to anticipate customer needs. Lagging data — like incoming orders — cannot accurately predict demand in the complex pharmaceutical market.
Additionally, shortened drug development timelines increase the challenge of balancing affordable pricing with the need to offset R&D costs. With revolutionary drugs coming to market faster than ever, historical pricing becomes irrelevant. Manufacturers need more data.
The following data points empower pharma companies to optimize revenue.
- Pricing data: competitor prices, payer preferences, and market research.
- Sales and market data: prescription trends, physician preferences, disease incidence, target populations, real-time sales, and performance data.
- Supply chain data: inventory levels, manufacturing capacity, logistics, ingredient availability, and lead times.
- R&D data: clinical trial outcomes, patient feedback, FDA approvals, risk evaluations, competitor strategies, and post-market surveillance data.
- Portfolio data: current product portfolio lifecycle, patent expiry dates, potential for new indications, and future R&D investments.
- Contract data: comprehensive visibility into customers, pricing terms, discounts, rebates, gross-to-net trends, and expected versus actual discounting.
Organizational silos, disparate sources, and manual processes hinder this data visibility. Fortunately, drug development isn’t the only process AI is improving. Manufacturers can employ AI-powered software to automate data gathering, integration, and analysis. These solutions evaluate a wide variety of internal and external data sources, allowing companies to uncover fluctuations, trends, and opportunities. This insight informs production, marketing, and pricing strategies while reducing administrative burden and improving data quality.
To capitalize on the promise of new drug therapies, pharma companies must couple R&D innovation with data management advancements. Comprehensive data integration, enhanced supply chain visibility, and predictive analytics will be critical to revenue optimization. Companies that fail to invest in data-driven operations risk failing to meet market demand or missing value-based pricing opportunities. Data and analytics will enable pharma manufacturers to balance patient access and business viability to lead the next generation of drug development.
