New technologies are boosting drug development. AI innovations are enabling pharmaceutical companies to bring safer and more effective medicines to market faster. This new era of medicine creates exciting new possibilities for patients and drugmakers, but it also brings new challenges.
The recent wave of highly effective weight loss drugs demonstrates the revenue opportunities and complexities facing drug manufacturers. Drugs such as Ozempic and Wegovy offer unprecedented benefits, skyrocketing demand and straining supply chains. AI could make high-demand drugs more common, which is why pharmaceutical manufacturers need to invest in data and analytics to maximize revenue.
The Promise of AI in Drug Discovery
Drug development typically takes more than a decade, but AI and machine learning can significantly shorten this time frame. Experts estimate that the market for AI in drug discovery will grow from $1.6 billion to $5.7 billion by 2028.
All solutions can collect and analyze massive data sets to quickly examine the effectiveness of millions of potential drug molecule combinations and identify the most promising candidates. Optimizing this preclinical phase reduces risks and shortens 40% delays. In one real-world example, AI reduced the time needed to select a preclinical candidate from 3-6 years to 18 monthssaving approximately $1.8 million.
Innovations in genomics and genetic sequencing will help identify new disease targets and biomarkers for more precise therapies tailored to specific diseases and patients. Advances in nanotechnology also provide drug developers with new options for personalized drug delivery mechanisms.
AI-powered tools can identify ideal clinical trial candidates, analyze trial results faster, discover subtle trends and patterns, and monitor and predict adverse events. These capabilities improve patient safety and data quality.
Apart from advanced data analysis, AI technology can also eliminate tedious administrative tasks and streamline workflows for more effective and efficient processes.
What accelerated drug development means for pharmaceutical manufacturers
Pharmaceutical companies have the opportunity to improve millions of lives with these new drug development tools. But manufacturers must be ready to meet demand and implement effective price management strategies.
Demand forecasting is particularly difficult for new and revolutionary drugs. Pharmaceutical companies must leverage all available data to anticipate customer needs. Delayed data, such as 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. As breakthrough drugs hit the market faster than ever, historical prices are no longer relevant. Manufacturers need more data.
The following data points help pharmaceutical companies optimize their 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 delivery times.
- R&D data: clinical trial results, patient feedback, FDA approvals, risk assessments, competitor strategies and post-market surveillance data.
- Portfolio data: current product portfolio life cycle, patent expiration dates, potential for new indications and future R&D investments.
- Contract Data: Complete visibility into customers, pricing terms, discounts, rebates, gross-to-net trends, and expected versus actual discounts.
Organizational silos, disparate sources and manual processes hinder this data visibility. Fortunately, drug development isn’t the only process that AI is improving. Manufacturers can use AI-based software to automate data collection, integration and analysis. These solutions evaluate a wide variety of internal and external data sources, allowing businesses to uncover fluctuations, trends and opportunities. This information informs production, marketing and pricing strategies while reducing administrative burden and improving data quality.
To realize the promise of new drug therapies, pharmaceutical companies must combine innovation in R&D with advances in data management. Comprehensive data integration, improved supply chain visibility and predictive analytics will be key to revenue optimization. Companies that don’t invest in data-driven operations risk failing to meet market demand or missing value-based pricing opportunities. Data and analytics will enable pharmaceutical manufacturers to balance patient access and commercial viability to lead the next generation of drug development.
