Framework to identify innovative sources of value creation from platform technologies
Bibliographic record
Abstract
Platform technologies are fundamentally reshaping the pharmaceutical industry, offering unprecedented potential for innovation across multiple therapeutic areas. However, traditional valuation models, focused on single-asset metrics, struggle to capture the full spectrum of value these technologies create. This paper presents a comprehensive framework for evaluating the innovative sources of value creation enabled by platform technologies throughout the drug development lifecycle. Through a systematic literature review, in-depth case studies, and framework development, we provide a structured methodology for capturing the diverse benefits of these technologies. Our findings reveal that platform technologies generate value across strategic, technical, and adaptive dimensions, requiring a multifaceted valuation approach. The proposed Platform Value Identification across Strategic, Technical, and Adaptive domains Framework defines key value drivers, specifies quantitative assessment metrics, and provides implementation guidance to inform strategic decision-making in research and development investment, portfolio management, and business development. Application of the framework to case studies of Alnylam's RNAi platform, Genentech's therapeutic antibody platform, and Moderna's mRNA platform demonstrates its broad utility and impact potential. By adopting this holistic, data-driven approach, stakeholders can better assess the long-term value and competitive advantages of well-implemented platform technologies, accelerating the development of transformative therapies for patients.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.014 | 0.015 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.026 | 0.015 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".