The problem with POL

Probability of Launch (POL) is a comprehensive metric designed to estimate the likelihood that a drug successfully completes all clinical trial phases, gains regulatory approval, and enters the market. Unlike Probability of Success (POS), POL extends its scope to incorporate factors beyond regulatory milestones, such as manufacturing readiness, market dynamics, and commercial strategy. While this broader focus provides a more holistic perspective, it also introduces significant limitations. POL struggles to account for the unpredictable and transformative developments that often define biotech and pharma, leading to a bias toward established therapies and a potential undervaluation of innovative approaches.

Defining POL

POL is calculated as a product of probabilities across the critical stages of drug development and market entry:

$$
POL = P_{clinical} \cdot P_{approval} \cdot P_{launch}
$$

Where:

• Pclinical​: Probability of completing all clinical trial phases successfully.
• Papproval​: Probability of securing regulatory approval.
• Plaunch​: Probability of successfully launching the product, incorporating manufacturing scalability, pricing, and market access.

By incorporating Plaunch​, POL aims to account for commercial realities that are often overlooked in metrics like POS.

A Comparative Example: GLP-1 for Weight Loss vs. Novel Parkinson’s Disease Therapy

Case 1: GLP-1 Therapy for Weight Loss

• Context: A GLP-1 receptor agonist, originally developed for diabetes, is being repurposed for weight loss. This indication has gained substantial attention due to its market potential and recent regulatory successes.
• Probabilities:
  • Pclinical=0.85: High likelihood of clinical success due to well-established safety and efficacy data in metabolic disorders.
  • Papproval=0.90: Favorable regulatory environment with clear endpoints and precedent.
  • Plaunch=0.95: Strong commercial strategy, robust manufacturing infrastructure, and high demand in the obesity market.

Calculation

$$
POL_{GLP1} = 0.85 \cdot 0.90 \cdot 0.95 = 0.727 \, (72.7\%)
$$

Case 2: First-in-Class Therapy for Parkinson’s Disease

• Context: A novel therapy targets mitochondrial dysfunction, a less-studied mechanism believed to contribute to Parkinson’s disease progression. The approach holds transformative potential but faces significant uncertainties.
• Probabilities:
  • Pclinical=0.50: Moderate trial success probability due to unproven efficacy and challenges in neurodegenerative indications.
  • Papproval=0.40: Regulatory uncertainty due to limited precedent and complex endpoints.
  • Plaunch=0.50: Significant manufacturing and pricing challenges in a relatively niche market.

Calculation

$$POL_{parkinsons} = 0.50 \cdot 0.40 \cdot 0.50 = 0.10 \, (10\%)
$$

Interpreting the Results

1. GLP-1 Therapy:
   • The POL of 72.7% reflects the maturity of GLP-1 receptor agonists as a modality and their expanding applications. Strong precedent and market demand make this an attractive investment opportunity.
   • However, competition within the weight loss market may compress long-term returns, even for successful launches.
2. Parkinson’s Therapy:
   • The POL of 10% highlights the challenges associated with targeting novel mechanisms in complex diseases. Despite the low probability, success would significantly shift the therapeutic landscape and establish a first-mover advantage.

This comparison illustrates how POL can overemphasize near-term success while underestimating the transformative potential of innovative therapies.

Critique of POL

1. Overemphasis on Established Pathways

POL heavily favors therapies with established mechanisms and clear regulatory pathways:

• Incremental Improvements: GLP-1 receptor agonists benefit from high Pclinical​ and Papproval values due to robust precedent, even in newer indications like weight loss.
• Limited Insight into Innovation: First-in-class therapies, like the Parkinson’s mitochondrial-targeting drug, are penalized despite their potential to redefine treatment paradigms.

2. Simplification of Market Entry Risks

While POL incorporates PlaunchP_{launch}Plaunch​, its application often oversimplifies commercial realities:

• Manufacturing and Scalability: Novel modalities, such as cell or gene therapies, face complex manufacturing hurdles that POL may underestimate. The Parkinson’s therapy, for instance, could require bespoke manufacturing setups.
• Market Access and Pricing: Therapies targeting high-cost indications, like neurodegenerative diseases, may struggle with payer acceptance. POL models frequently fail to capture these nuanced dynamics.

3. Static Nature of Probability Estimates

POL is often treated as a static metric, failing to adapt to real-time developments:

• Regulatory Evolution: Accelerated approval pathways or changes in regulatory expectations can significantly alter Papproval​.
• Market Shifts: Competitive dynamics, such as new GLP-1 entrants for weight loss, can rapidly change Plaunch prospects. POL often lags in reflecting such developments.

4. Penalizing Transformative Potential

POL relies on historical data to estimate probabilities, which inherently biases it against therapies with limited precedent:

• Emerging Mechanisms: The Parkinson’s therapy targeting mitochondrial dysfunction suffers from a low Pclinical​ due to limited prior validation, despite its potential to address unmet needs.
• Risk Aversion: By focusing on near-term probabilities, POL discourages investment in transformative science, favoring therapies like GLP-1 receptor agonists that expand on established pathways.

Illustrative Shortcomings of POL

Bias Toward Market Trends

The high POL for GLP-1 therapies reflects their current dominance in the weight loss market but may fail to account for saturation or diminishing returns as competition intensifies. Conversely, the Parkinson’s therapy, while carrying greater risk, could create a new market segment if successful.

Inadequate Consideration of Long-Term Impact

POL’s focus on immediate probabilities often overlooks therapies with the potential for outsized long-term impact. While the Parkinson’s therapy has a lower POL, its success would significantly alter treatment paradigms, creating opportunities for follow-on innovations.

Conclusion: The Limitations of POL

Probability of Launch (POL) provides a broader framework than metrics like POS by incorporating commercial readiness and market factors. However, its reliance on historical data, emphasis on established mechanisms, and inability to capture long-term transformative potential limit its utility. While POL accurately highlights the advantages of therapies like GLP-1 receptor agonists in weight loss, it systematically undervalues high-risk, high-reward projects such as novel Parkinson’s therapies. Decision-makers must supplement POL with dynamic, scenario-based analyses that account for both immediate probabilities and the broader impact of therapeutic breakthroughs. This approach ensures a more balanced evaluation of risk and opportunity in biotech and pharma.