The billion-dollar gamble of drug development is getting a scientific makeover with predictive scoring systems
Imagine a high-stakes journey where only 6 out of 100 travelers reach their destination. This isn't an extreme wilderness expedition—it's the reality of drug development, where a staggering 94% of potential medicines fail between laboratory discovery and patient application.
The consequences are monumental: billions of dollars spent and decades of research often lead to dead ends, particularly in complex areas like Alzheimer's disease and mental health disorders.
What if researchers could calculate the odds of success before embarking on this costly journey? Enter the "translatability score"—a revolutionary assessment system acting as a GPS for drug development.
How compelling are the laboratory results?
What evidence exists from human studies?
Are there measurable indicators to track treatment effectiveness?
Can the treatment be tailored to specific patient subgroups?
The initial translatability score represented a major advance in predictive drug development, but researchers soon noticed a critical flaw: it didn't account for fundamental differences between disease areas.
This realization sparked the development of customized scoring templates for six distinct disease categories: cardiovascular, oncology, psychiatric, anti-viral, anti-bacterial/fungal, and monogenetic diseases 1 3 .
| Disease Area | Increased Weight For | Decreased Weight For |
|---|---|---|
| Oncology | Animal models, biomarkers, personalized medicine | - |
| Psychiatrics | Model compounds, clinical trials, endpoint strategy | Animal models, biomarkers, personalized medicine |
| Anti-viral | In vitro data, personalized medicine | Animal models |
| Anti-bacterial/fungal | Animal models, personalized medicine | - |
| Monogenetic orphans | Genetics, personalized medicine | Model compounds |
The COVID-19 pandemic created an unprecedented opportunity: dozens of potential vaccines and treatments were developing at record speed, allowing researchers to test the translatability score in real-time.
Scientists performed both prospective and retrospective case studies of COVID-19 treatments and vaccines, applying the translatability score before Phase III trial results were known 2 7 .
The scoring adaptations for vaccines were particularly insightful: increasing weights for in vitro data and animal models, while reducing weights for human genetics and increasing emphasis on disease subclassification 2 7 .
| Compound | Translatability Score | Predicted Success | Actual Outcome |
|---|---|---|---|
| CVnCoV (Curevac) | 4.40 | Fair to good chance | Successful |
| Covifenz (Medicago) | 3.52 | Moderate chance | Successful |
| Vidprevtyn (Sanofi) | 4.21 | Fair to good chance | Successful |
| Camostat mesylate | 1.98 | Very high risk | Failed |
Identifies patient subgroups most likely to respond. Critical in oncology, less used in psychiatry 8 .
Tests efficacy and safety before human trials. Strong predictive value in some fields, weak in others 8 .
Provides measurable indicators of treatment effect. Quality varies significantly by disease area 8 .
Advanced human cell-based systems for testing. Particularly valuable when animal models are weak 8 .
Recapitulates specific genetic aspects of disease. Essential for monogenetic diseases and oncology 8 .
Computer simulations that predict drug behavior. Emerging technology with growing importance 8 .
The implications of effective translatability scoring extend far beyond predicting success rates. By identifying specific weaknesses in a drug development program, the score provides researchers with a targeted improvement roadmap 2 5 .
Can prioritize research portfolios, allocating resources to candidates with the highest likelihood of success 2 .
Can make more informed investment decisions based on quantitative risk assessment 2 .
Can identify and address weaknesses in their developmental strategy early in the process 2 .
Can anticipate which treatment approaches are most promising for future health crises 2 .
The translatability score doesn't eliminate risk from drug development, but it does what all good forecasting systems do: it helps navigate uncertainty with eyes wide open. In the high-stakes journey from laboratory discovery to patient application, that clearer vision might just make all the difference.