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Estd 4.22.2023
Introduction
The pharmaceutical industry stands at a critical inflection point. Traditional drug discovery methods have become increasingly time-consuming and costly, with development timelines averaging 10-15 years and costs exceeding $2.6 billion per successful drug. Meanwhile, artificial intelligence promises to revolutionize this landscape by dramatically accelerating research timelines and improving success rates. But this raises a complex question for both AI providers and pharmaceutical companies: how should AI solutions in drug discovery be priced?
This article explores the emerging pricing models for AI in drug discovery, with particular focus on approaches that balance success rates against research timelines—two critical metrics that determine the ultimate value of these technologies.
The Economics of Drug Discovery
Before diving into pricing models, it's important to understand the economics that drive decision-making in pharmaceutical R&D.
Traditional Economics
In conventional drug development:
- Only about 12% of drugs that enter clinical trials ultimately receive FDA approval, according to a recent analysis by BIO Industry Analysis
- Each month of delay in bringing a drug to market can cost pharmaceutical companies between $600,000 and $8 million in lost revenue opportunity, depending on the therapeutic area and market potential
- The longer development timeline increases the cost of capital and delays the realization of returns on investment
AI's Value Proposition
AI technologies promise to transform these economics by:
- Improving target identification and validation
- Enhancing molecule design and optimization
- Predicting toxicity issues earlier
- Identifying suitable patient populations for clinical trials
- Reducing the overall failure rate throughout the pipeline
According to a 2022 report from Deloitte, AI-powered drug discovery could potentially reduce early-phase research timelines by up to 50% and improve success rates by 10-15 percentage points when fully integrated into the R&D process.
Current Pricing Models for AI in Drug Discovery
Several pricing approaches have emerged in the market:
1. Subscription-Based Models
Many AI drug discovery platforms operate on a subscription basis, typically charging:
- Annual license fees ranging from $500,000 to several million dollars
- Tiered access to different capabilities
- Volume-based pricing for computational resources
While straightforward, this model fails to align incentives around success rates and timelines, as the AI provider gets paid regardless of outcomes.
2. Milestone-Based Pricing
This approach ties payments to research achievements:
- Base access fee plus milestone payments
- Triggers include successful target validation, lead compound identification, preclinical study success, and more
- Better aligns incentives but may not fully capture the time-value component
3. Royalty and Revenue-Share Models
A longer-term approach:
- Smaller upfront costs
- AI providers receive a percentage (typically 1-5%) of eventual product revenue
- May include success-based milestones during development
- Creates strong alignment but delays most compensation for the AI provider
Emerging Success Rate vs. Timeline Models
The most innovative pricing approaches explicitly balance the tradeoff between speed and success:
The Acceleration Premium Model
This model prices AI services based on demonstrated timeline reductions:
- Baseline price for standard implementation
- Premium multipliers for accelerated timelines (e.g., 1.5x premium for 30% timeline reduction)
- Success-based components for quality assurance
- Clawback provisions if accelerated timelines lead to quality issues
Insitro, a machine learning-driven drug discovery company, has implemented variations of this model in its partnerships with major pharmaceutical companies like Gilead and Bristol Myers Squibb.
The Success Rate Escalator
This approach focuses on improving the probability of success:
- Base pricing tier for access to AI platform
- Pricing multipliers that increase with demonstrated improvements in project success rates
- Rebates for projects that fail to meet minimum success thresholds
- Built-in incentives to optimize for quality rather than just speed
BenevolentAI has pioneered versions of this model in its partnership with AstraZeneca, where compensation increases as AI-identified targets progress successfully through various validation stages.
The Timeline-Adjusted Value Share Model
Perhaps the most sophisticated approach:
- Initial implementation fee plus modest ongoing access fees
- Value sharing based on both:
- Timeline reduction (e.g., months saved × average monthly value of earlier market entry)
- Success rate improvement (increased probability of technical and regulatory success)
- Complex but highly aligned model that rewards AI providers proportionally to actual value creation
Exscientia, which has partnerships with companies including Sanofi and Bristol Myers Squibb, has implemented versions of this model where compensation increases based on both the speed of discovery and the quality of candidates identified.
Implementation Challenges
While these models are theoretically compelling, they face practical implementation challenges:
Measurement Complexity
How do you establish reliable baselines for:
- "Normal" development timelines without AI?
- Expected success rates for a specific project?
- The counterfactual of what would have happened without AI?
Attribution Problems
Drug discovery is multifaceted, raising questions about:
- How much of any success is attributable specifically to the AI component?
- What role did human expertise play in improving outcomes?
- How should value be divided among multiple technology providers?
Risk Allocation
Different pricing models shift risk between parties:
- Pure subscription models place all risk on the pharmaceutical company
- Pure revenue-share models place most risk on the AI provider
- Hybrid models attempt to balance risk, but negotiations can be complex
Best Practices for Structuring AI Drug Discovery Pricing
Based on emerging market practices, several recommendations stand out:
1. Hybrid Approaches Work Best
Most successful partnerships employ hybrid models with:
- Moderate upfront access fees to ensure commitment
- Performance-based components tied to specific metrics
- Long-term value sharing for alignment
2. Define Clear Metrics and Measurement Protocols
Successful agreements:
- Establish clear baseline metrics before implementation
- Use objective and measurable success indicators
- Include regular assessment periods
- Employ third-party verification when needed
3. Include Flexibility Mechanisms
Given the rapid evolution of AI technologies:
- Build in periodic pricing review points
- Allow for adjustment based on demonstrated value
- Include mechanisms to incorporate new capabilities as they emerge
Case Study: Recursion Pharmaceuticals and Bayer
A 2021 partnership between Recursion Pharmaceuticals and Bayer illustrates these principles in action:
- $30 million upfront payment to Recursion
- $20 million equity investment
- Up to $100 million in research milestone payments
- More than $550 million in potential commercial milestone payments
- Tiered royalties on future sales
This structure balances immediate value recognition with long-term incentive alignment, while incorporating both timeline and success rate considerations in the milestone structures.
Conclusion
As AI continues to transform drug discovery, pricing models are evolving to better reflect the unique value these technologies bring. The most sophisticated approaches explicitly address the fundamental tension between accelerating research timelines and improving success rates.
For SaaS providers in the AI drug discovery space, building pricing models that appropriately balance these factors will be critical to commercial success. For pharmaceutical executives, understanding these models is essential for making informed decisions about technology investments that could dramatically improve R&D productivity.
The future likely belongs to pricing models that create genuine alignment between technology providers and drug developers, with both parties rewarded for what truly matters: bringing effective therapies to patients more quickly and more reliably than was previously possible.