The problem of PTS

Probability of Technical Success (PTS) is a metric that evaluates the likelihood that a drug candidate will successfully navigate clinical trials. Unlike broader metrics such as Probability of Success (POS) or Probability of Launch (POL), PTS focuses narrowly on the technical aspects of clinical development, including efficacy, safety, and adherence to trial protocols. While this specificity makes it useful for evaluating clinical feasibility, PTS suffers from significant limitations. Its focus on isolated technical challenges often overlooks broader, interconnected risks and fails to account for the dynamic, uncertain nature of clinical research, particularly in innovative or underexplored therapeutic areas.

Defining PTS

PTS is calculated as the product of success probabilities across the clinical trial phases:

$$
PTS = P_{phase\_I} \cdot P_{phase\_II} \cdot P_{phase\_III}
$$

Where:

• Pphase I​: Probability of successfully completing Phase I (safety and tolerability).
• Pphase II: Probability of successfully completing Phase II (efficacy and dosage optimization).
• Pphase III: Probability of successfully completing Phase III (confirmation of efficacy and safety in larger populations).

PTS is distinct from broader metrics like POS by excluding regulatory, commercial, and market factors, narrowing its focus to technical feasibility within clinical trials.

A Comparative Example: Incremental Oncology Therapy vs. First-in-Class CNS Drug

Case 1: Incremental Oncology Therapy Targeting EGFR

• Context: A new therapy targeting the well-understood EGFR pathway aims to provide incremental improvements over existing cancer treatments. The pathway has extensive precedent in oncology.
• Probabilities:
  • Pphas I=0.90: High likelihood of safety and tolerability based on precedent.
  • Pphase II=0.80: Moderate efficacy predictability in Phase II due to well-defined biomarkers.
  • Pphase III=0.75: Strong likelihood of confirming efficacy and safety in Phase III.

Calculation

$$
PTS_{EGFR} = 0.90 \cdot 0.80 \cdot 0.75 = 0.54 \, (54\%)
$$

Case 2: First-in-Class Drug for CNS Disorders

• Context: A first-in-class therapy targets a novel CNS mechanism for a rare neurodegenerative disease. Limited historical data and high variability in CNS trial outcomes create substantial uncertainty.
• Probabilities:
  • Pphase I=0.70: Moderate safety risk due to novel mechanism of action.
  • Pphase II=0.50: Efficacy uncertainty due to limited patient cohorts and unvalidated endpoints.
  • Pphase III=0.30: High risk of failure in larger trials due to variability in patient populations.

Calculation

$$
PTS_{CNS} = 0.70 \cdot 0.50 \cdot 0.30 = 0.105 \, (10.5\%)
$$

Interpreting the Results

1. EGFR-Targeting Therapy:
   • The PTS of 54% reflects the well-understood nature of the EGFR pathway and its extensive clinical precedent.
   • Incremental improvements in oncology benefit from established biomarkers and streamlined trial designs.
2. CNS Therapy:
   • The PTS of 10.5% underscores the challenges associated with novel mechanisms and rare diseases, particularly in the CNS space.
   • While the PTS is low, the potential impact of success could significantly redefine treatment paradigms for the disease.

This comparison highlights PTS’s utility in evaluating technical feasibility but also its bias toward well-established therapies and pathways.

Critique of PTS

1. Overemphasis on Technical Feasibility

PTS focuses exclusively on technical challenges within clinical trials, ignoring broader interdependencies:

• Regulatory Considerations:
  Trial outcomes are closely tied to regulatory standards, which can influence endpoints and success criteria. PTS fails to capture these dynamics.
• Market and Competitive Factors:
  Technical success alone does not guarantee downstream viability. For example, an oncology therapy with a high PTS may fail to achieve market differentiation.

2. Bias Toward Established Pathways

PTS relies heavily on historical benchmarks, favoring well-understood mechanisms:

• Incremental Therapies:
  Therapies like EGFR inhibitors benefit from high PTS values due to extensive clinical precedent, even if their innovation is minimal.
• Novel Therapies:
  First-in-class drugs, such as CNS therapies targeting new mechanisms, face lower PTS values despite their potential to address unmet needs.

3. Static Nature of PTS

PTS is often treated as a static metric, failing to account for the dynamic nature of clinical trials:

• Interim Data and Adaptive Designs:
  Emerging data or changes in trial design can significantly alter phase probabilities. PTS does not adapt to these real-time shifts.
• Evolving Standards:
  Regulatory agencies increasingly accept surrogate endpoints or accelerated pathways, but PTS models often fail to incorporate these evolving standards.

4. Penalizing Transformative Science

By focusing narrowly on technical success, PTS systematically undervalues innovative therapies:

• Novel Mechanisms:
  Therapies addressing underexplored mechanisms, such as CNS drugs, face low PTS values due to a lack of historical precedent, discouraging investment in transformative science.
• High-Risk Indications:
  Rare or complex diseases often feature lower PTS values due to recruitment challenges and endpoint variability, despite their potential for outsized impact.

Illustrative Shortcomings of PTS

1. Rewarding Incremental Progress

An EGFR-targeting therapy with high PTS may achieve technical success but fail to deliver meaningful clinical differentiation or address unmet needs.

2. Penalizing First-in-Class Innovations

The CNS therapy, despite its low PTS, could redefine treatment paradigms for neurodegenerative diseases if successful. PTS underrepresents this transformative potential.

3. Ignoring External Influences

PTS does not account for external factors like advocacy, regulatory innovation, or market conditions that can influence trial success. For example:

• A rare disease therapy may benefit from patient advocacy groups that facilitate recruitment and trial success.
• Regulatory programs like accelerated approvals can improve the likelihood of success but are excluded from PTS calculations.

Conclusion: The Limitations of PTS

Probability of Technical Success (PTS) offers a focused framework for evaluating clinical trial feasibility, but its narrow scope and reliance on historical data limit its utility. By emphasizing technical challenges without considering broader interdependencies, PTS systematically undervalues transformative therapies and high-risk indications. While useful for assessing incremental progress, PTS must be complemented with dynamic, multidimensional analyses that capture the complexities of clinical development and the broader implications of success.